WO2020135127A1 - 一种行人识别方法及装置 - Google Patents

一种行人识别方法及装置 Download PDF

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Publication number
WO2020135127A1
WO2020135127A1 PCT/CN2019/125667 CN2019125667W WO2020135127A1 WO 2020135127 A1 WO2020135127 A1 WO 2020135127A1 CN 2019125667 W CN2019125667 W CN 2019125667W WO 2020135127 A1 WO2020135127 A1 WO 2020135127A1
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Prior art keywords
pedestrian
feature
node
target
similarity
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PCT/CN2019/125667
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English (en)
French (fr)
Inventor
朱铖恺
张寿奎
武伟
闫俊杰
黄潇莹
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深圳市商汤科技有限公司
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Priority to KR1020217008615A priority Critical patent/KR20210047917A/ko
Priority to JP2021500852A priority patent/JP7171884B2/ja
Priority to SG11202011791SA priority patent/SG11202011791SA/en
Publication of WO2020135127A1 publication Critical patent/WO2020135127A1/zh
Priority to US17/113,949 priority patent/US20210089799A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition
    • G06V40/23—Recognition of whole body movements, e.g. for sport training
    • G06V40/25—Recognition of walking or running movements, e.g. gait recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/70—Multimodal biometrics, e.g. combining information from different biometric modalities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection

Definitions

  • the present disclosure relates to the field of computer vision technology, and in particular, to a pedestrian recognition method and device.
  • Pedestrian recognition technology plays an important role in the field of security monitoring such as smart cities and public security, and is also an important topic in the field of computer vision.
  • Pedestrian recognition is a challenging technology.
  • Pedestrian recognition technologies in related technologies are often based on human characteristics such as the clothes of pedestrians and the attributes of people. Typical technologies may include pedestrian re-identification (Person ReID), for example.
  • Person ReID pedestrian re-identification
  • human body characteristics are often not unique, such as pedestrians changing clothes and so on.
  • the present disclosure provides a pedestrian recognition method and device.
  • a pedestrian recognition method including:
  • the feature database includes a plurality of pedestrian feature nodes
  • the pedestrian feature nodes include facial features, human body features corresponding to pedestrian images, and relationship features with other pedestrian feature nodes.
  • the technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects:
  • the pedestrian recognition method provided by the embodiments of the present disclosure may search for the image of the target pedestrian from the feature database based on the joint retrieval of facial features and human features.
  • the joint retrieval method based on facial features and human features can take advantage of the unique advantages of facial features, as well as the recognition advantages of human features in special situations such as obscured faces and blurred faces.
  • the feature database may include relationship features between the pedestrian feature node and other pedestrian feature nodes. In this way, one of the pedestrian feature nodes may be used to search for a pedestrian feature node associated with the pedestrian feature node. Based on this, the calculation amount of pedestrian search can be greatly reduced, and the search efficiency can be improved.
  • the relationship feature is set to be determined according to the following parameters: facial image quality value, human image quality value, human face feature, and human feature.
  • the technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: the face image quality value and the human body image quality value are used as parameters for calculating the associated feature, which may improve the accuracy of the calculation result of the relationship feature .
  • the relationship feature includes a similar node association relationship
  • the similar node association relationship is set to be determined in the following manner:
  • the technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: determining the association relationship of similar nodes based on the face image quality value, the human image quality value, the face characteristics, and the body characteristics, and according to the attributes between the face characteristics and the body characteristics Difference, set the priority of facial features higher than the priority of human features, and accurately determine the association relationship of similar nodes.
  • the at least one target node of the image feature is obtained from a feature database, and pedestrian images corresponding to the at least one target node are used as the image of the target pedestrian ,include:
  • the image feature as a target feature node, and determining at least one search path from the target feature node to the pedestrian feature node, where the search path is formed by connecting multiple pedestrian feature nodes having the similar node association relationship;
  • Pedestrian images corresponding to the at least one target node are used as the target pedestrian images.
  • the technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: the similarity between the target feature node and the pedestrian feature node is determined based on multiple search paths, and the determination method of the similarity may be optimized.
  • the at least one target node of the image feature is obtained from a feature database, and pedestrian images corresponding to the at least one target node are used as the image of the target pedestrian ,include:
  • a pedestrian image corresponding to each of the at least one target node is used as the target pedestrian image.
  • the technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: some nodes are removed from the at least one similar node in a manner of providing post-processing.
  • the selecting at least one target node from the at least one similar node includes:
  • the technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: filtering out similar nodes whose facial features deviate too much from the central value of the face clustering based on the cluster center value from the at least one similar node, and the remaining Similar nodes are used as target nodes.
  • the method before the clearing the second set of similar nodes from the at least one similar node, the method further includes:
  • At least one human feature node is selected from the at least one similar node, the human face feature in the human feature node has a zero value, and the human feature has a non-zero value;
  • the technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: further filtering out human bodies in similar nodes whose facial features are zero and human features are non-zero based on the clustering central value from the at least one similar node Nodes with features that deviate from the central value of human clustering.
  • the method further includes:
  • an action trajectory of the target pedestrian is acquired, and the action trajectory includes time information and/or location information.
  • the technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: Based on the pedestrian's action trajectory, the daily activities of the target pedestrian may be obtained, which is of great value to the field of public security and psychological analysis
  • the method further includes:
  • the image features of the new pedestrian image are used as new pedestrian feature nodes and updated to the feature database.
  • the feature database may be continuously updated so that the feature database maintains the latest information.
  • a pedestrian recognition device including:
  • An image feature acquisition module configured to acquire image features of a target pedestrian image, where the image features include facial features and human features;
  • a target node obtaining module configured to obtain at least one target node of the image feature from a feature database, and use pedestrian images corresponding to the at least one target node as the image of the target pedestrian;
  • the feature database includes a plurality of pedestrian feature nodes
  • the pedestrian feature nodes include facial features, human body features corresponding to pedestrian images, and relationship features with other pedestrian feature nodes.
  • the relationship feature is set to be determined according to the following parameters: facial image quality value, human image quality value, human face feature, and human feature.
  • the relationship feature includes a similar node association relationship
  • the similar node association relationship is set to be determined in the following manner:
  • the target node acquisition module includes:
  • a path determination submodule configured to use the image feature as a target feature node, and determine at least one search path from the target feature node to the pedestrian feature node, where the search path is composed of multiple related relationships of the similar nodes Pedestrian feature nodes are connected;
  • a path score determination submodule configured to determine a minimum value in the similarity between two adjacent pedestrian feature nodes in the search path, and use the minimum value as the path score of the search path;
  • a node similarity determination submodule configured to determine a maximum value among path scores of the at least one search path, and use the maximum value as the similarity between the target feature node and the pedestrian feature node;
  • a target node determination submodule configured to use at least one pedestrian feature node whose similarity with the target feature node is greater than or equal to the preset face similarity threshold or the preset human similarity threshold as the target feature node At least one target node, and use pedestrian images corresponding to the at least one target node as the image of the target pedestrian.
  • the target node acquisition module includes:
  • a similar node search submodule configured to search for at least one similar node of the image feature from the feature database based on the relationship features of the plurality of pedestrian feature nodes;
  • a target node selection sub-module for selecting at least one target node from the at least one similar node
  • the pedestrian image acquisition submodule is configured to use pedestrian images corresponding to the at least one target node as the target pedestrian image.
  • the target node selection submodule includes:
  • a face center value determining unit configured to determine a face clustering center value of face features in the at least one similar node
  • a node filtering unit configured to filter at least one human face human feature node from the at least one similar node, and the human face features and human body features in the human face human feature nodes are non-zero values;
  • a node dividing unit used to determine the face similarity between the face feature and the face clustering center value in the at least one face human feature node, and the face similarity is greater than or equal to the preset similarity
  • Nodes with a degree threshold are divided into a first set of similar nodes, and nodes with a face similarity less than the preset similarity threshold are divided into a second set of similar nodes;
  • the node removal unit is configured to remove the second set of similar nodes from the at least one similar node, and use the pedestrian images corresponding to the at least one similar node after removal as the target pedestrian images.
  • the target node selection sub-module further includes:
  • a human body center value determining unit configured to determine a first human body clustering center value of human body features in the first similarity node set, and a second human body clustering center value of human body features in the second similarity node set;
  • a human body node screening unit configured to screen out at least one human body feature node from the at least one similar node, the human face feature in the human body feature node has a zero value, and the human body feature has a non-zero value;
  • the similarity determination unit is configured to determine the first human body similarity between the human body feature and the first human body clustering center value in the at least one human body feature node, and the second human body clustering center value respectively The second human similarity;
  • a node adding unit is used to add a human body feature node corresponding to the second human body similarity when the second human body similarity is greater than the first human body similarity.
  • the device further includes:
  • a pedestrian trajectory acquisition module is used to acquire the action trajectory of the target pedestrian based on the image of the target pedestrian, the action trajectory including time information and/or location information.
  • the device further includes:
  • a new data acquisition module used to extract image features of the new pedestrian image in the case of acquiring a new pedestrian image
  • the data updating module is used to update the image features of the new pedestrian image as new pedestrian feature nodes to the feature database.
  • an electronic device including:
  • Memory for storing processor executable instructions
  • the processor is configured to perform the above pedestrian recognition method.
  • a non-transitory computer-readable storage medium which, when instructions in the storage medium are executed by a processor, enables the processor to execute the aforementioned pedestrian recognition method.
  • a computer program includes computer readable code, and when the computer readable code runs in an electronic device, the processor in the electronic device executes In order to realize the above pedestrian recognition method.
  • Fig. 1 is a flowchart of a method for pedestrian recognition according to an exemplary embodiment.
  • Fig. 2 is a scene diagram shown according to an exemplary embodiment.
  • Fig. 3 is a block diagram of a device according to an exemplary embodiment.
  • Fig. 4 is a block diagram of a device according to an exemplary embodiment.
  • Fig. 5 is a block diagram of a device according to an exemplary embodiment.
  • Pedestrian recognition technology of related technologies is often based on face recognition technology or human body recognition technology.
  • Pedestrian recognition technology based on face recognition technology often recognizes target pedestrians through the facial features of pedestrians.
  • the captured facial images of pedestrians often have obstructions, side angles, too far away, etc. Therefore, the way of identifying target pedestrians through facial features often also has a low recall rate And accuracy.
  • the pedestrian recognition method provided by the present disclosure may construct a feature database based on facial features and human features based on the joint retrieval of human faces and human bodies. Based on the facial features and human features of the target pedestrian, the facial features and human features similar to the facial features and human features of the target pedestrian can be searched from the feature database, and the similar facial features The pedestrian image corresponding to the human body feature is used as the target pedestrian image.
  • FIG. 1 is a method flowchart of an embodiment of a pedestrian recognition method provided by the present disclosure.
  • the present disclosure provides method operation steps as shown in the following embodiments or drawings, more or less operation steps may be included in the method based on conventional or without creative labor. Among the steps that do not logically need a causal relationship, the execution order of these steps is not limited to the execution order provided by the embodiments of the present disclosure.
  • An embodiment of the present disclosure provides a pedestrian recognition method, which can be applied to any image processing apparatus, for example, the method can be applied to a terminal device or a server, or can also be applied to other processing devices, where the terminal device Can include user equipment (User Equipment, UE), mobile devices, user terminals, terminals, cellular phones, cordless phones, personal digital processing (Personal Digital Assistant, PDA), handheld devices, computing devices, in-vehicle devices, wearable devices, etc.
  • the pedestrian recognition method may be implemented by the processor calling computer-readable instructions stored in the memory.
  • FIG. 1 an embodiment of the pedestrian recognition method provided by the present disclosure is shown in FIG. 1, and the method may include:
  • S101 Acquire image features of a target pedestrian image, where the image features include facial features and human features.
  • S103 Acquire at least one target node of the image feature from the feature database, and use a pedestrian image corresponding to the at least one target node as the target pedestrian image;
  • the feature database includes a plurality of pedestrian feature nodes
  • the pedestrian feature nodes include facial features, human body features corresponding to pedestrian images, and relationship features with other pedestrian feature nodes.
  • a target pedestrian image used as a search basis may be obtained.
  • the target pedestrian image may include, for example, Zhang San’s ID photo, life photo, and street Take photos, portraits, etc.
  • the target pedestrian image may include a face image, a human body image, or a human face image. Based on this, image features can be obtained from the target pedestrian image, and the image features can include facial features and human features.
  • facial features and human body features can be expressed using feature vectors.
  • the facial feature vectors can include multiple components such as Euclidean distance, curvature, and angle between key points of the human face.
  • the human feature can be It includes various components such as proportions, postures, and clothing characteristics of human body parts. The disclosure does not limit the extraction method of face features and human features.
  • At least one target node of the image feature may be acquired from a preset feature database based on the image feature.
  • the feature database may include multiple pedestrian feature nodes, and the pedestrian feature nodes include facial features, human features, and relationship features with other pedestrian feature nodes corresponding to pedestrian images.
  • the pedestrian feature nodes have a one-to-one correspondence with pedestrian images. For example, if the feature database can include 1 million pedestrian feature nodes, the 1 million pedestrian feature nodes correspond to 100 Ten thousand pedestrian images. Then, the purpose of the embodiments of the present disclosure is to search out the target pedestrian image from the one million pedestrian images.
  • the pedestrian image may include a human face image, a human body image, a human face image, and based on this, the human face characteristics and human body characteristics of the pedestrian image may be extracted, and the human face characteristics and human body characteristics may be set In the pedestrian feature node corresponding to the pedestrian image.
  • the relationship feature with other pedestrian feature nodes may be set to be determined according to the face feature and the human feature.
  • the relationship feature includes a similar node association relationship, and the similar node association relationship includes two pedestrian feature nodes having a high degree of similarity, that is, the two pedestrian feature nodes are likely to be feature nodes of the same pedestrian .
  • Another pedestrian feature node can be searched through one of the pedestrian feature nodes.
  • the face features of the two pedestrian feature nodes are non-zero values and the similarity between the face features of the two pedestrian feature nodes is greater than or equal to the preset face similarity threshold To determine that the two pedestrian feature nodes are related to similar nodes.
  • the body features of the two pedestrian feature nodes are non-zero values and the similarity between the body features of the two pedestrian feature nodes is greater than or equal to the preset body similarity threshold, it is determined
  • the two pedestrian feature nodes are similar node association relationships.
  • the similarity between the facial features or the human features can be calculated using a feature vector.
  • the similarity can be a cosine value between two feature vectors. The disclosure does not limit the calculation method of the similarity between two features.
  • the relationship feature is set to be determined according to the following parameters: face image quality value, human image quality value, human face feature, and human feature.
  • face image quality value can be calculated based on the parameters of the human face 3-dimensional posture, the degree of blurring of the picture, the exposure quality, etc.
  • human body image quality value can be calculated according to the parameters such as the degree of occlusion, the degree of crowding, the integrity of the subject Calculated.
  • the pedestrian feature node may further include a face image quality value and a human body image quality value.
  • the image features of the target pedestrian image may also include a face image quality value and a human body image quality value.
  • the similarity between the face features of the two pedestrian feature nodes may be calculated first. This is due to the uniqueness and accuracy of facial features, so the priority of facial features can be set higher than the priority of human features.
  • the similarity between the facial features of the two pedestrian feature nodes may be determined when the smaller facial image quality value of the two pedestrian feature nodes is greater than or equal to the preset facial image quality threshold. That is to say, when the face features in the two pedestrian feature nodes are non-zero values, and the face image quality values in the two pedestrian feature nodes are greater than or equal to the preset face image quality threshold, it is determined that the Similarity between face features of two pedestrian feature nodes. If it is calculated that the similarity between the face features is greater than or equal to a preset face similarity threshold, it is determined that the two pedestrian feature nodes are similar node association relationships.
  • the smaller face image quality value of the two pedestrian feature nodes is less than a preset face image quality threshold, it may be determined whether the human features of the two pedestrian feature nodes are non-zero values.
  • the calculation may be performed The similarity between human features of two pedestrian feature nodes. In a case where the similarity between the human body features is greater than or equal to a preset human body similarity threshold, it may be determined that the two pedestrian feature nodes are similar node association relationships.
  • the preset face image quality threshold for the setting of the preset face image quality threshold, the preset human body image quality threshold, the preset face similarity threshold, and the preset human body similarity threshold, reference may be made to an empirical value, It can also be obtained based on sample data statistics, which is not limited in this disclosure.
  • a network-type relationship graph may be formed between the multiple pedestrian feature nodes. Through one of the pedestrian feature nodes, a pedestrian feature node with a similar node association relationship can be searched from the feature database.
  • the expression manner of the feature database may include a network structure such as a heterogeneous graph.
  • the The image feature is used as a target feature node, and at least one search path that reaches the target feature node from the target feature node is determined, and the search path is formed by connecting a plurality of pedestrian feature nodes having the association relationship of the similar nodes.
  • a minimum value in the similarity between two adjacent pedestrian feature nodes in the search path may be determined, and the minimum value may be used as the path score of the search path.
  • a maximum value among the path scores of the at least one search path may be determined, and the maximum value is used as the similarity between the target feature node and the pedestrian feature node.
  • the target feature node is set to node A
  • nodes B-H are pedestrian feature nodes in the feature database.
  • node C and node D in path 1 have similar node associations between node D and node B
  • node E in path 3 There are similar node associations with node F, node F and node G, node G and node H, and node H and node B.
  • the direct similarity between node A and node B is 0.5.
  • node B will not be determined as node A Similar nodes.
  • both node A and node B are characteristics of the target pedestrian, but node A may correspond to the front image of the target pedestrian wearing black clothes, and node B may correspond to the side image of the target pedestrian wearing yellow clothes, then, The direct similarity between node A and node B may be relatively low. However, through other related nodes to reach B, you can find the close relationship between node A and node B. For example, in path 1, node C is the frontal image of the target pedestrian's face, and node D is the frontal image of the target pedestrian wearing that yellow dress.
  • the path score of each path may be calculated separately, and the path score may include the minimum value of the similarity between two adjacent pedestrian feature nodes in the path.
  • the path score of path 1 is 0.6
  • the path score of path 2 is 0.5
  • the path score of path 3 is 0.8.
  • the largest path score of the three paths is 0.8
  • node A and node B can be determined
  • the similarity between them is 0.8, which is greater than 0.7. Therefore, node A and node B are target nodes of target feature node A.
  • the feature database can be searched in the same manner as the method in the above embodiment, at least one target node corresponding to the target feature node is searched, and pedestrian images corresponding to the at least one target node as the target pedestrian Image.
  • the process of acquiring at least one target node of the image feature from a feature database, and using pedestrian images corresponding to the at least one target node as the image of the target pedestrian in Based on the relationship features of the plurality of pedestrian feature nodes, after searching for at least one similar node of the image feature from the feature database, filtering out those facial features from the at least one similar node that is too far away from the face cluster Similar nodes with similar center values, and take the remaining similar nodes as target nodes.
  • the method of acquiring the similar node reference may be made to the method of searching for the target node B of the node A in the foregoing example.
  • the face clustering center value of the face features in the at least one similar node can be determined. Then, at least one human face human feature node is selected from the at least one similar node, and the human face features and human body features in the human face human feature nodes are non-zero values. Then, a node whose face feature deviates too far from the center value of the face clustering may be filtered from the at least one face human feature node.
  • the face similarity between the face feature in the at least one face human feature node and the face clustering center value may be calculated separately, and the face similarity is greater than or equal to a preset similarity threshold
  • the nodes of are divided into a first set of similar nodes, and the nodes whose face similarity is less than the preset similarity threshold are divided into a second set of similar nodes.
  • the similar nodes in the second similar node set have a high possibility that they are not nodes corresponding to the target pedestrian. Therefore, the second set of similar nodes may be cleared from the at least one similar node, and the pedestrian images corresponding to the at least one similar node after clearing may be used as the target pedestrian images.
  • the at least one similar node may be further filtered to filter out human features in similar nodes whose facial features are zero-valued and human features are non-zero-valued, deviating from the clustering center of the human body Value node.
  • the first human clustering center value of human body features in the first similarity node set and the second human clustering center value of human body features in the second similarity node set may be calculated.
  • at least one human feature node may be selected from the at least one similar node, and the human face feature in the human feature node has a zero value and the human feature has a non-zero value.
  • the second similar node set is the node set to be filtered out. If the second human body similarity is greater than the first human body similarity, it indicates that the human body characteristic also deviates from the human body characteristic of the target pedestrian. Therefore, the human body feature nodes corresponding to the second human body similarity greater than the first human body similarity may be added to the second similarity node set. Thereafter, the second set of similar nodes may be cleared from the at least one similar node.
  • multiple target pedestrian images are often used for feature search.
  • feature search can be performed on the multiple target pedestrian images, respectively, and at least one target node is obtained.
  • at least one target node obtained separately may be merged, and a pedestrian image corresponding to the at least one target node after the merger may be used as the image of the target pedestrian.
  • the action trajectory of the target pedestrian may be acquired based on the image of the target pedestrian, the action trajectory including time information and/or location information .
  • the action trajectory of the target pedestrian includes, for example, 10:30, October 1, 2018: Guanqian Street, Suzhou ⁇ 11:03, October 1, 2018: Guanqian Street, Suzhou ⁇ 10, 2018 12:50 on January 1st: XX parking lot in Suzhou ⁇ ... ⁇ 21:37 on October 1, 2018: XX community in Suzhou city. Based on the above action trajectory, the daily activities of the target pedestrian can be obtained, which is of great value in the field of public security and psychological analysis.
  • the feature database may be updated.
  • the image frames in the surveillance video may be extracted.
  • feature extraction can be performed on the image frame to extract image features of the image frame, and the image features include facial features and human features.
  • the image features in the image frame are used as new pedestrian feature nodes and updated to the feature database.
  • the pedestrian recognition methods provided by the various embodiments of the present disclosure can search for the image of the target pedestrian from the feature database based on the joint retrieval of facial features and human features.
  • the joint retrieval method based on facial features and human features can take advantage of the unique advantages of facial features, as well as the recognition advantages of human features in special situations such as obscured faces and blurred faces.
  • the feature database may include relationship features between the pedestrian feature node and other pedestrian feature nodes. In this way, one of the pedestrian feature nodes may be used to search for a pedestrian feature node associated with the pedestrian feature node. Based on this, the calculation amount of pedestrian search can be greatly reduced, and the search efficiency can be improved.
  • FIG. 3 shows a block diagram of a pedestrian recognition device according to an embodiment of the present disclosure. As shown in FIG. 3, the device 300 includes:
  • the image feature acquisition module 301 is used to acquire image features of a target pedestrian image, where the image features include facial features and human features;
  • a target node obtaining module 303 configured to obtain at least one target node of the image feature from a feature database, and use pedestrian images corresponding to the at least one target node as the image of the target pedestrian;
  • the feature database includes a plurality of pedestrian feature nodes
  • the pedestrian feature nodes include facial features, human body features corresponding to pedestrian images, and relationship features with other pedestrian feature nodes.
  • the relationship feature is set to be determined according to the following parameters: facial image quality value, human image quality value, human face feature, and human feature.
  • the relationship feature includes a similar node association relationship
  • the similar node association relationship is set to be determined in the following manner:
  • the target node acquisition module includes:
  • a path determination submodule configured to use the image feature as a target feature node, and determine at least one search path from the target feature node to the pedestrian feature node, where the search path is composed of multiple related relationships of the similar nodes Pedestrian feature nodes are connected;
  • a path score determination submodule configured to determine a minimum value in the similarity between two adjacent pedestrian feature nodes in the search path, and use the minimum value as the path score of the search path;
  • a node similarity determination submodule configured to determine a maximum value among path scores of the at least one search path, and use the maximum value as the similarity between the target feature node and the pedestrian feature node;
  • a target node determination submodule configured to use at least one pedestrian feature node whose similarity with the target feature node is greater than or equal to the preset face similarity threshold or the preset human similarity threshold as the target feature node At least one target node, and use pedestrian images corresponding to the at least one target node as the image of the target pedestrian.
  • the target node acquisition module includes:
  • a similar node search submodule configured to search for at least one similar node of the image feature from the feature database based on the relationship features of the plurality of pedestrian feature nodes;
  • a target node selection sub-module for selecting at least one target node from the at least one similar node
  • the pedestrian image acquisition submodule is configured to use pedestrian images corresponding to the at least one target node as the target pedestrian image.
  • the target node selection submodule includes:
  • a face center value determining unit configured to determine a face clustering center value of face features in the at least one similar node
  • a node filtering unit configured to filter at least one human face human feature node from the at least one similar node, and the human face features and human body features in the human face human feature nodes are non-zero values;
  • a node dividing unit used to determine the face similarity between the face feature and the face clustering center value in the at least one face human feature node, and the face similarity is greater than or equal to the preset similarity
  • Nodes with a degree threshold are divided into a first set of similar nodes, and nodes with a face similarity less than the preset similarity threshold are divided into a second set of similar nodes;
  • the node removal unit is configured to remove the second set of similar nodes from the at least one similar node, and use the pedestrian images corresponding to the at least one similar node after removal as the target pedestrian images.
  • the target node selection sub-module further includes:
  • a human body center value determining unit configured to determine a first human body clustering center value of human body features in the first similarity node set, and a second human body clustering center value of human body features in the second similarity node set;
  • a human body node screening unit configured to screen out at least one human body feature node from the at least one similar node, the human face feature in the human body feature node has a zero value, and the human body feature has a non-zero value;
  • the similarity determination unit is configured to determine the first human body similarity between the human body feature and the first human body clustering center value in the at least one human body feature node, and the second human body clustering center value respectively The second human similarity;
  • a node adding unit is used to add a human body feature node corresponding to the second human body similarity when the second human body similarity is greater than the first human body similarity.
  • the device further includes:
  • a pedestrian trajectory acquisition module is used to acquire the action trajectory of the target pedestrian based on the image of the target pedestrian, the action trajectory including time information and/or location information.
  • the device further includes:
  • a new data acquisition module used to extract image features of the new pedestrian image in the case of acquiring a new pedestrian image
  • the data updating module is used to update the image features of the new pedestrian image as new pedestrian feature nodes to the feature database.
  • An embodiment of the present disclosure also provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured as the method described in each of the foregoing embodiments.
  • the electronic device may be provided as a terminal, server, or other form of device.
  • Fig. 4 is a block diagram of an electronic device 800 according to an exemplary embodiment.
  • the electronic device 800 may be a terminal such as a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, and a personal digital assistant.
  • the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, and a sensor component 814 , ⁇ 816.
  • the processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
  • the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps in the above method.
  • the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components.
  • the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
  • the memory 804 is configured to store various types of data to support operation at the electronic device 800. Examples of these data include instructions for any application or method for operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc.
  • the memory 804 may be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable and removable Programmable read only memory (EPROM), programmable read only memory (PROM), read only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read only memory
  • EPROM erasable and removable Programmable read only memory
  • PROM programmable read only memory
  • ROM read only memory
  • magnetic memory flash memory
  • flash memory magnetic disk or optical disk.
  • the power supply component 806 provides power to various components of the electronic device 800.
  • the power component 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 800.
  • the multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touch, swipe, and gestures on the touch panel. The touch sensor may not only sense the boundary of the touch or sliding action, but also detect the duration and pressure related to the touch or sliding operation.
  • the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
  • the audio component 810 is configured to output and/or input audio signals.
  • the audio component 810 includes a microphone (MIC).
  • the microphone is configured to receive an external audio signal.
  • the received audio signal may be further stored in the memory 804 or transmitted via the communication component 816.
  • the audio component 810 further includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module.
  • the peripheral interface module may be a keyboard, a click wheel, or a button. These buttons may include, but are not limited to: home button, volume button, start button, and lock button.
  • the sensor component 814 includes one or more sensors for providing the electronic device 800 with status assessment in various aspects.
  • the sensor component 814 can detect the on/off state of the electronic device 800, and the relative positioning of the components, for example, the component is the display and keypad of the electronic device 800, and the sensor component 814 can also detect the electronic device 800 or the electronic device 800.
  • the position of the component changes, the presence or absence of user contact with the electronic device 800, the orientation or acceleration/deceleration of the electronic device 800, and the temperature change of the electronic device 800.
  • the sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact.
  • the sensor component 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor component 814 may further include an acceleration sensor, a gyro sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • the communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices.
  • the electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof.
  • the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the electronic device 800 may be used by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field Programming gate array (FPGA), controller, microcontroller, microprocessor or other electronic components are used to implement the above method.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGA field Programming gate array
  • controller microcontroller, microprocessor or other electronic components are used to implement the above method.
  • a non-volatile computer-readable storage medium is also provided, for example, a memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to complete the above method.
  • Fig. 5 is a block diagram of an electronic device 1900 according to an exemplary embodiment.
  • the electronic device 1900 may be provided as a server.
  • the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by the memory 1932, for storing instructions executable by the processing component 1922, such as application programs.
  • the application programs stored in the memory 1932 may include one or more modules each corresponding to a set of instructions.
  • the processing component 1922 is configured to execute instructions to perform the above method.
  • the electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to the network, and an input output (I/O) interface 1958 .
  • the electronic device 1900 can operate an operating system based on the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.
  • a non-volatile computer-readable storage medium is also provided, for example, a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the above method.
  • the present disclosure may be a system, method, and/or computer program product.
  • the computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for causing the processor to implement various aspects of the present disclosure.
  • the computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), and erasable programmable read only memory (EPROM (Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a computer on which instructions are stored
  • RAM random access memory
  • ROM read only memory
  • EPROM erasable programmable read only memory
  • SRAM static random access memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disk
  • memory stick floppy disk
  • mechanical encoding device such as a computer on which instructions are stored
  • the convex structure in the hole card or the groove and any suitable combination of the above.
  • the computer-readable storage medium used here is not to be interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, optical pulses through fiber optic cables), or through wires The transmitted electrical signal.
  • the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • the network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .
  • the computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or in one or more programming languages Source code or object code written in any combination.
  • the programming languages include object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.
  • Computer readable program instructions can be executed entirely on the user's computer, partly on the user's computer, as an independent software package, partly on the user's computer and partly on a remote computer, or completely on the remote computer or server carried out.
  • the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider to pass the Internet connection).
  • electronic circuits such as programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLA), are personalized by utilizing the state information of computer-readable program instructions.
  • Computer-readable program instructions are executed to implement various aspects of the present disclosure.
  • These computer-readable program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, or other programmable data processing device, thereby producing a machine that causes these instructions to be executed by the processor of a computer or other programmable data processing device A device that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram is generated.
  • the computer-readable program instructions may also be stored in a computer-readable storage medium. These instructions cause the computer, programmable data processing apparatus, and/or other devices to work in a specific manner. Therefore, the computer-readable medium storing the instructions includes An article of manufacture that includes instructions to implement various aspects of the functions/acts specified in one or more blocks in the flowchart and/or block diagram.
  • the computer-readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other equipment, so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , So that the instructions executed on the computer, other programmable data processing device, or other equipment implement the functions/acts specified in one or more blocks in the flowchart and/or block diagram.
  • each block in the flowchart or block diagram may represent a part of a module, program segment, or instruction that contains one or more Executable instructions.
  • the functions marked in the blocks may also occur in an order different from that marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can also be executed in reverse order, depending on the functions involved.
  • each block in the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented with a dedicated hardware-based system that performs specified functions or actions Or, it can be realized by a combination of dedicated hardware and computer instructions.

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Abstract

一种行人识别的方法及装置。所述方法包括：获取目标行人图像的图像特征，所述图像特征包括人脸特征和人体特征（S101）；从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像（S103）；其中，所述特征数据库中包括多个行人特征节点，所述行人特征节点中包括行人图像对应的人脸特征、人体特征以及与其他行人特征节点之间的关系特征。该方法可以大大降低行人搜索的计算量，提高搜索效率。

Description

一种行人识别方法及装置
本公开要求在2018年12月29日提交中国专利局、申请号为201811637119.4、申请名称为“一种行人识别方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本公开中。
技术领域
本公开涉及计算机视觉技术领域，尤其涉及一种行人识别方法及装置。
背景技术
行人识别技术在智慧城市、公安等安防监控领域具有重要的作用，同时也是计算机视觉领域的重要课题。行人识别是具有挑战性的技术，相关技术中的行人识别技术往往基于行人的衣着、人物属性等人体特征，典型的技术例如可以包括行人重识别(Person ReID)。但是，由于很多环境因素和外在因素的影响，人体特征往往唯一性不高，如行人更换衣着等等。
发明内容
为克服相关技术中存在的问题，本公开提供一种行人识别方法及装置。
根据本公开实施例的第一方面，提供一种行人识别方法，包括：
获取目标行人图像的图像特征，所述图像特征包括人脸特征和人体特征；
从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像；
其中，所述特征数据库中包括多个行人特征节点，所述行人特征节点中包括行人图像对应的人脸特征、人体特征以及与其他行人特征节点之间的关系特征。
本公开的实施例提供的技术方案可以包括以下有益效果：本公开实施例提供的行人识别方法，可以基于人脸特征和人体特征联合检索的方式从特征数据库中搜索出目标行人的图像。一方面，基于人脸特征和人体特征联合检索的方式，可以即利用了人脸特征的唯一性优势，也利用了在人脸被遮挡、人脸模糊等特殊情况下人体特征的识别优势。另一方面，所述特征数据库可以包括所述行人特征节点与其他行人特征节点之间的关系特征，这样，可以通过其中一个行人特征节点搜索到与之有关联关系的行人特征节点。基于此，可以大大降低行人搜索的计算量，提高搜索效率。
可选的，在本公开的一个实施例中，所述关系特征被设置为根据下述参数确定：人脸图像质量值、人体图像质量值、人脸特征、人体特征。
本公开的实施例提供的技术方案可以包括以下有益效果：将所述人脸图像质量值和所述人体图像质量值作为计算所述关联特征的参数，可以提升所述关系特征计算结果的准确性。
可选的，在本公开的一个实施例中，所述关系特征包括相似节点关联关系，所述相似节点关联关系被设置为按照下述方式确定：
在两个行人特征节点中较小的人脸图像质量值大于等于预设人脸图像质量阈值的情况下，确定所述两个行人特征节点的人脸特征之间的相似度；
在所述人脸特征之间的相似度大于等于预设人脸相似度阈值的情况下，确定所述两个行人特征节点为相似节点关联关系；
在所述两个行人特征节点中较小的人脸图像质量值小于预设人脸图像质量阈值，且所述两个行人特征节点中较小的人体图像质量值大于等于人体图像质量阈值的情况下，确定 所述两个行人特征节点的人体特征之间的相似度；
在所述人体特征之间的相似度大于等于预设人体相似度阈值的情况下，确定所述两个行人特征节点为相似节点关联关系。
本公开的实施例提供的技术方案可以包括以下有益效果：基于人脸图像质量值、人体图像质量值、人脸特征、人体特征确定相似节点关联关系，根据人脸特征和人体特征之间的属性差异，设置人脸特征的优先级高于人体特征的优先级，准确地确定相似节点的关联关系。
可选的，在本公开的一个实施例中，所述从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像，包括：
将所述图像特征作为目标特征节点，确定所述目标特征节点到达所述行人特征节点的至少一条搜索路径，所述搜索路径由具有所述相似节点关联关系的多个行人特征节点连接而成；
确定所述搜索路径中相邻两个行人特征节点之间的相似度中的最小值，并将所述最小值作为所述搜索路径的路径分值；
确定所述至少一条搜索路径的路径分值中的最大值，并将所述最大值作为所述目标特征节点与所述行人特征节点的相似度；
将与所述目标特征节点的相似度大于等于所述预设人脸相似度阈值或者所述预设人体相似度阈值的至少一个行人特征节点作为所述目标特征节点的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
本公开的实施例提供的技术方案可以包括以下有益效果：基于多条搜索路径的方式确定所述目标特征节点与所述行人特征节点之间的相似度，可以优化相似度的确定方式。
可选的，在本公开的一个实施例中，所述从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像，包括：
基于所述多个行人特征节点的关系特征，从所述特征数据库中搜索出所述图像特征的至少一个相似节点；
从所述至少一个相似节点中选择出至少一个目标节点；
将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
本公开的实施例提供的技术方案可以包括以下有益效果：提供后处理的方式从所述至少一个相似节点中清除一些节点。
可选的，在本公开的一个实施例中，所述从所述至少一个相似节点中选择出至少一个目标节点，包括：
确定所述至少一个相似节点中人脸特征的人脸聚类中心值；
从所述至少一个相似节点中筛选出至少一个人脸人体特征节点，所述人脸人体特征节点中的人脸特征和人体特征为非零值；
分别确定所述至少一个人脸人体特征节点中人脸特征与所述人脸聚类中心值之间的人脸相似度，将所述人脸相似度大于等于预设相似度阈值的节点划分至第一相似节点集合，将所述人脸相似度小于所述预设相似度阈值的节点划分至第二相似节点集合；
从所述至少一个相似节点中清除所述第二相似节点集合，并将清除后的所述至少一个相似节点分别对应的行人图像作为所述目标行人的图像。
本公开的实施例提供的技术方案可以包括以下有益效果：基于聚类中心值从所述至少一个相似节点中过滤掉一些人脸特征过于偏离人脸聚类中心值的相似节点，并将剩余的相似节点作为目标节点。
可选的，在本公开的一个实施例中，在所述从所述至少一个相似节点中清除所述第二 相似节点集合之前，所述方法还包括：
确定所述第一相似节点集合中人体特征的第一人体聚类中心值、所述第二相似节点集合中人体特征的第二人体聚类中心值；
从所述至少一个相似节点中筛选出至少一个人体特征节点，所述人体特征节点中的人脸特征为零值、人体特征为非零值；
分别确定所述至少一个人体特征节点中人体特征与所述第一人体聚类中心值之间的第一人体相似度、与所述第二人体聚类中心值之间的第二人体相似度；
将所述第二人体相似度大于所述第一人体相似度时所对应的人体特征节点添加至所述第二相似节点集合中。
本公开的实施例提供的技术方案可以包括以下有益效果：基于聚类中心值从所述至少一个相似节点中进一步过滤掉人脸特征为零值、人体特征为非零值的相似节点中的人体特征偏离人体聚类中心值的节点。
可选的，在本公开的一个实施例中，所述方法还包括：
基于所述目标行人的图像，获取所述目标行人的行动轨迹，所述行动轨迹包括时间信息和/或位置信息。
本公开的实施例提供的技术方案可以包括以下有益效果：基于行人的行动轨迹，可以获取所述目标行人的日常活动，对于公安、心理分析领域具有重要的价值
可选的，在本公开的一个实施例中，所述方法还包括：
在获取到新行人图像的情况下，提取所述新行人图像的图像特征；
将所述新行人图像的图像特征作为新的行人特征节点，更新至所述特征数据库中。
本公开的实施例提供的技术方案可以包括以下有益效果：可以不断地更新所述特征数据库，使得所述特征数据库保持最新的信息。
根据本公开实施例的第二方面，提供一种行人识别装置，包括：
图像特征获取模块，用于获取目标行人图像的图像特征，所述图像特征包括人脸特征和人体特征；
目标节点获取模块，用于从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像；
其中，所述特征数据库中包括多个行人特征节点，所述行人特征节点中包括行人图像对应的人脸特征、人体特征以及与其他行人特征节点之间的关系特征。
可选的，在本公开的一个实施例中，所述关系特征被设置为根据下述参数确定：人脸图像质量值、人体图像质量值、人脸特征、人体特征。
可选的，在本公开的一个实施例中，所述关系特征包括相似节点关联关系，所述相似节点关联关系被设置为按照下述方式确定：
在两个行人特征节点中较小的人脸图像质量值大于等于预设人脸图像质量阈值的情况下，确定所述两个行人特征节点的人脸特征之间的相似度；
在所述人脸特征之间的相似度大于等于预设人脸相似度阈值的情况下，确定所述两个行人特征节点为相似节点关联关系；
在所述两个行人特征节点中较小的人脸图像质量值小于预设人脸图像质量阈值，且所述两个行人特征节点中较小的人体图像质量值大于等于人体图像质量阈值的情况下，确定所述两个行人特征节点的人体特征之间的相似度；
在所述人体特征之间的相似度大于等于预设人体相似度阈值的情况下，确定所述两个行人特征节点为相似节点关联关系。
可选的，在本公开的一个实施例中，所述目标节点获取模块包括：
路径确定子模块，用于将所述图像特征作为目标特征节点，确定所述目标特征节点到达所述行人特征节点的至少一条搜索路径，所述搜索路径由具有所述相似节点关联关系的 多个行人特征节点连接而成；
路径分值确定子模块，用于确定所述搜索路径中相邻两个行人特征节点之间的相似度中的最小值，并将所述最小值作为所述搜索路径的路径分值；
节点相似度确定子模块，用于确定所述至少一条搜索路径的路径分值中的最大值，并将所述最大值作为所述目标特征节点与所述行人特征节点的相似度；
目标节点确定子模块，用于将与所述目标特征节点的相似度大于等于所述预设人脸相似度阈值或者所述预设人体相似度阈值的至少一个行人特征节点作为所述目标特征节点的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
可选的，在本公开的一个实施例中，所述目标节点获取模块包括：
相似节点搜索子模块，用于基于所述多个行人特征节点的关系特征，从所述特征数据库中搜索出所述图像特征的至少一个相似节点；
目标节点选取子模块，用于从所述至少一个相似节点中选择出至少一个目标节点；
行人图像获取子模块，用于将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
可选的，在本公开的一个实施例中，所述目标节点选取子模块包括：
人脸中心值确定单元，用于确定所述至少一个相似节点中人脸特征的人脸聚类中心值；
节点筛选单元，用于从所述至少一个相似节点中筛选出至少一个人脸人体特征节点，所述人脸人体特征节点中的人脸特征和人体特征为非零值；
节点划分单元，用于分别确定所述至少一个人脸人体特征节点中人脸特征与所述人脸聚类中心值之间的人脸相似度，将所述人脸相似度大于等于预设相似度阈值的节点划分至第一相似节点集合，将所述人脸相似度小于所述预设相似度阈值的节点划分至第二相似节点集合；
节点清除单元，用于从所述至少一个相似节点中清除所述第二相似节点集合，并将清除后的所述至少一个相似节点分别对应的行人图像作为所述目标行人的图像。
可选的，在本公开的一个实施例中，所述目标节点选取子模块还包括：
人体中心值确定单元，用于确定所述第一相似节点集合中人体特征的第一人体聚类中心值、所述第二相似节点集合中人体特征的第二人体聚类中心值；
人体节点筛选单元，用于从所述至少一个相似节点中筛选出至少一个人体特征节点，所述人体特征节点中的人脸特征为零值、人体特征为非零值；
相似度确定单元，用于分别确定所述至少一个人体特征节点中人体特征与所述第一人体聚类中心值之间的第一人体相似度、与所述第二人体聚类中心值之间的第二人体相似度；
节点添加单元，用于将所述第二人体相似度大于所述第一人体相似度时所对应的人体特征节点添加至所述第二相似节点集合中。
可选的，在本公开的一个实施例中，所述装置还包括：
行人轨迹获取模块，用于基于所述目标行人的图像，获取所述目标行人的行动轨迹，所述行动轨迹包括时间信息和/或位置信息。
可选的，在本公开的一个实施例中，所述装置还包括：
新数据获取模块，用于在获取到新行人图像的情况下，提取所述新行人图像的图像特征；
数据更新模块，用于将所述新行人图像的图像特征作为新的行人特征节点，更新至所述特征数据库中。
根据本公开实施例的第三方面，提供一种电子设备，包括：
处理器；
用于存储处理器可执行指令的存储器；
其中，所述处理器被配置为执行上述行人识别方法。
根据本公开实施例的第四方面，提供一种非临时性计算机可读存储介质，当所述存储介质中的指令由处理器执行时，使得处理器能够执行上述的行人识别方法。
根据本公开实施例的第五方面，提供一种计算机程序，所述计算机程序包括计算机可读代码，当所述计算机可读代码在电子设备中运行时，所述电子设备中的处理器执行用于实现上述行人识别方法。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。
图1是根据一示例性实施例示出的一种行人识别方法的流程图。
图2是根据一示例性实施例示出的一种场景图。
图3是根据一示例性实施例示出的一种装置的框图。
图4是根据一示例性实施例示出的一种装置的框图。
图5是根据一示例性实施例示出的一种装置的框图。
具体实施方式
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本公开相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的、本公开的一些方面相一致的装置和方法的例子。
为了方便本领域技术人员理解本公开实施例提供的技术方案，下面先对技术方案实现的技术环境进行说明。
相关技术的行人识别技术往往基于人脸识别技术或者人体识别技术，利用基于人脸识别技术的行人识别技术往往通过行人的脸部特征识别出目标行人。但是在实际应用场景中，如街景中，捕捉到的行人面部图像往往具有遮挡物、侧面角度、距离太远等等，因此，通过脸部特征识别目标行人的方式往往也具有较低的召回率和准确率。
基于类似于上文所述的实际技术需求，本公开提供的行人识别方法可以基于人脸人体联合检索的方式，构建基于人脸特征和人体特征的特征数据库。基于目标行人的人脸特征和人体特征，可以从所述特征数据库中搜索出与所述目标行人的人脸特征和人体特征相似的人脸特征和人体特征，并将所述相似的人脸特征和人体特征所对应的行人图像作为所述目标行人的图像。
下面结合附图1对本公开所述的行人识别方法进行详细的说明。图1是本公开提供的行人识别方法的一种实施例的方法流程图。虽然本公开提供了如下述实施例或附图所示的方法操作步骤，但基于常规或者无需创造性的劳动在所述方法中可以包括更多或者更少的操作步骤。在逻辑性上不存在必要因果关系的步骤中，这些步骤的执行顺序不限于本公开实施例提供的执行顺序。
本公开实施例提供了一种行人识别方法，其可以应用在任意的图像处理装置中，例如，该方法可以应用在终端设备或服务器中，或者也可以应用在其它处理设备中，其中，终端设备可以包括用户设备(User Equipment，UE)、移动设备、用户终端、终端、蜂窝电话、 无绳电话、个人数字处理(Personal Digital Assistant，PDA)、手持设备、计算设备、车载设备、可穿戴设备等。在一些可能的实现方式中，该行人识别方法可以通过处理器调用存储器中存储的计算机可读指令的方式来实现。
具体的，本公开提供的行人识别方法的一种实施例如图1所示，所述方法可以包括：
S101：获取目标行人图像的图像特征，所述图像特征包括人脸特征和人体特征。
S103：从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像；
其中，所述特征数据库中包括多个行人特征节点，所述行人特征节点中包括行人图像对应的人脸特征、人体特征以及与其他行人特征节点之间的关系特征。
本公开实施例中，可以获取用于作为搜索基础的目标行人图像，在一个示例中，若目标行人为张三，则所述目标行人图像例如可以包括张三的身份证件照片、生活照片、街拍照片、写真等等。所述目标行人图像中可以包括人脸图像，可以包括人体图像，也可以包括人脸人体图像。基于此，可以从所述目标行人图像中获取图像特征，所述图像特征可以包括人脸特征和人体特征。即，当所述目标行人图像中只包括人脸图像时，可以获取到人脸特征，即所述图像特征中人脸特征为非零值，人体特征为零值；当所述目标行人图像中只包括人体图像时，可以获取到人体特征，即所述图像特征中人脸特征为零值，人体特征为非零值；当所述目标行人图像中包括人脸人体图像时，可以获取到人脸特征和人体特征，即所述图像特征中人脸特征和人体特征为非零值。其中，所述人脸特征、所述人体特征可以利用特征向量表达，例如，人脸特征向量可以包括人脸关键点之间的欧氏距离、曲率、角度等多种分量，所述人体特征可以包括人体部位的比例、姿态、衣着特征等多种分量。本公开对于人脸特征、人体特征的提取方式不做限制。
本公开实施例中，在获取到所述目标行人图像的所述图像特征之后，可以基于所述图像特征，从预设特征数据库中获取所述图像特征的至少一个目标节点。所述特征数据库中可以包括多个行人特征节点，所述行人特征节点中包括行人图像对应的人脸特征、人体特征以及与其他行人特征节点之间的关系特征。在一个实施例中，所述行人特征节点与行人图像具有一一对应的关系，例如，若所述特征数据库中可以包括100万个行人特征节点，则所述100万个行人特征节点对应于100万个行人图像。那么，本公开实施例的目的在于从这100万个行人图像搜索出所述目标行人的图像。同样地，所述行人图像中可以包括人脸图像、人体图像、人脸人体图像，基于此，可以提取所述行人图像的人脸特征和人体特征，并将所述人脸特征和人体特征设置于所述行人图像所对应的行人特征节点中。
本公开实施例中，所述与其他行人特征节点之间的关系特征可以被设置为根据人脸特征、人体特征确定。所述关系特征包括相似节点关联关系，所述相似节点关联关系包括两个行人特征节点之间具有较高的相似度，即所述两个行人特征节点为同一行人的特征节点的可能性较大。通过所述相似节点关联关系，可以通过其中一个行人特征节点搜索到另一个行人特征节点。在一个实施例中，在两个行人特征节点的人脸特征均为非零值且所述两个行人特征节点的人脸特征之间的相似度大于等于预设人脸相似度阈值的情况下，确定所述两个行人特征节点为相似节点关联关系。在另一个实施例中，在两个行人特征节点的人体特征均为非零值且所述两个行人特征节点的人体特征之间的相似度大于等于预设人体相似度阈值的情况下，确定所述两个行人特征节点为相似节点关联关系。在本公开的一个实施例中，所述人脸特征或者所述人体特征之间的相似度可以利用特征向量计算得到，例如，所述相似度可以为两个特征向量之间的余弦值，本公开对于两个特征之间的相似度的计算方式不做限制。
在实际的应用场景中，图像质量在人脸识别、人体识别中具有比较重要的影响因素，当图像质量较高时，人脸识别、人体识别的准确性随之增高，当图像质量较低时，人脸识别、人体识别的准确性随之降低。基于此，在本公开的一个实施例中，所述关系特征被设 置为根据下述参数确定：人脸图像质量值、人体图像质量值、人脸特征、人体特征。其中，所述人脸图像质量值可以根据人脸3维姿态、图片模糊程度、曝光好坏等参数计算得到，所述人体图像质量值可以根据遮挡程度、拥挤程度、主体人的完整程度等参数计算得到。在此情况下，所述行人特征节点中还可以包括人脸图像质量值、人体图像质量值。相应地，所述目标行人图像的图像特征还可以包括人脸图像质量值、人体图像质量值。
相应地，在确定所述相似节点关联关系的过程中，可以首先计算两个行人特征节点的人脸特征之间的相似度。这是由于人脸特征的唯一性和准确性，因此，可以设置人脸特征的优先级高于人体特征的优先级。具体地，可以在两个行人特征节点中较小的人脸图像质量值大于等于预设人脸图像质量阈值的情况下，确定所述两个行人特征节点的人脸特征之间的相似度。也就是说，当两个行人特征节点中的人脸特征均为非零值，且这两个行人特征节点中的人脸图像质量值均大于等于预设人脸图像质量阈值时，确定所述两个行人特征节点的人脸特征之间的相似度。若计算得到所述人脸特征之间的相似度大于等于预设人脸相似度阈值，则确定所述两个行人特征节点为相似节点关联关系。
在所述两个行人特征节点中较小的人脸图像质量值小于预设人脸图像质量阈值的情况下，可以确定所述两个行人特征节点的人体特征是否为非零值。在确定所述两个行人特征节点中的人体特征均为非零值，且所述两个行人特征节点中较小的人体图像质量值小于预设人体图像质量阈值的情况下，可以计算所述两个行人特征节点的人体特征之间的相似度。在所述人体特征之间的相似度大于等于预设人体相似度阈值的情况下，可以确定所述两个行人特征节点为相似节点关联关系。需要说明的是，对于所述预设人脸图像质量阈值、所述预设人体图像质量阈值、所述预设人脸相似度阈值、所述预设人体相似度阈值的设置可以参考经验值，也可以根据样本数据统计得到，本公开对此不做限制。
在确定所述多个行人特征节点中具有相似节点关联关系的行人特征节点之后，所述多个行人特征节点之间可以形成网络式的关系图。通过其中一个行人特征节点，可以从所述特征数据库中搜索出与之具有相似节点关联关系的行人特征节点。所述特征数据库的表达方式可以包括异构图等网络结构。
本公开实施例中，在从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像的过程中，可以将所述图像特征作为目标特征节点，确定所述目标特征节点到达所述行人特征节点的至少一条搜索路径，所述搜索路径由具有所述相似节点关联关系的多个行人特征节点连接而成。在确定所述至少一条搜索路径之后，可以确定所述搜索路径中相邻两个行人特征节点之间的相似度中的最小值，并将所述最小值作为所述搜索路径的路径分值。在确定各个搜索路径的路径分值之后，可以确定所述至少一条搜索路径的路径分值中的最大值，并将所述最大值作为所述目标特征节点与所述行人特征节点的相似度。最后，将与所述目标特征节点的相似度大于等于所述预设人脸相似度阈值或者所述预设人体相似度阈值的至少一个行人特征节点作为所述目标特征节点的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
下面结合图2说明上述实施例方法，如图2所示，设置所述目标特征节点为节点A，节点B-H为所述特征数据库中的行人特征节点。从节点A到节点B共有三条路径，分别为路径1、路径2、路径3，其中路径1中的节点C与节点D、节点D与节点B之间具有相似节点关联关系，路径3中节点E与节点F、节点F与节点G、节点G与节点H、节点H与节点B之间具有相似节点关联关系。根据路径2中的示意，节点A和节点B之间的直接相似度为0.5，若设置的预设人脸相似度阈值和预设人体相似度阈值为0.7，则不会确定节点B为节点A的相似节点。基于实际的应用场景，节点A和节点B均为目标行人的特征，但是节点A可能对应于目标行人穿着黑色衣服的正面图像，而节点B可能对应于目标行人穿着黄色衣服的侧面图像，那么，节点A与节点B的直接相似度可能比较低。 但是，通过其他关联节点到达B，可以发现节点A与节点B之间的紧密关联性。例如在路径1中，节点C为目标行人的脸部正面图像，节点D为目标行人穿着那件黄色衣服的正面图像。基于此，可以优化节点A与节点B之间的相似度计算方式。在一个实施例中，可以分别计算各个路径的路径分值，所述路径分值可以包括路径中相邻两个行人特征节点之间的相似度中的最小值。例如，路径1的路径分值为0.6，路径2的路径分值为0.5，路径3的路径分值为0.8，其中三个路径中最大的路径分值为0.8，那么可以确定节点A与节点B之间的相似度为0.8，大于0.7，因此，节点A与节点B为目标特征节点A的目标节点。
基于此，可以通过与上述实施例方法相同的方式搜索所述特征数据库，搜索出与所述目标特征节点的至少一个目标节点，将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
在本公开的一个实施例中，在从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像的过程中，在基于所述多个行人特征节点的关系特征，从所述特征数据库中搜索出所述图像特征的至少一个相似节点之后，从所述至少一个相似节点中过滤掉那些人脸特征过于偏离人脸聚类中心值的相似节点，并将剩余的相似节点作为目标节点。其中，所述相似节点的获取方式可以参考上述示例中搜索节点A的目标节点B的方式。具体的过滤方式，可以确定所述至少一个相似节点中人脸特征的人脸聚类中心值。然后，从所述至少一个相似节点中筛选出至少一个人脸人体特征节点，所述人脸人体特征节点中的人脸特征和人体特征为非零值。然后，可以从所述至少一个人脸人体特征节点中过滤掉人脸特征过于偏离人脸聚类中心值的节点。具体地，可以分别计算所述至少一个人脸人体特征节点中人脸特征与所述人脸聚类中心值之间的人脸相似度，将所述人脸相似度大于等于预设相似度阈值的节点划分至第一相似节点集合，将所述人脸相似度小于所述预设相似度阈值的节点划分至第二相似节点集合。其中，所述第二相似节点集合中的相似节点具有很大可能性不是所述目标行人对应的节点。因此，可以从所述至少一个相似节点中清除所述第二相似节点集合，并将清除后的所述至少一个相似节点分别对应的行人图像作为所述目标行人的图像。
在本公开的一个实施例中，还可以对所述至少一个相似节点进行进一步过滤，以过滤掉人脸特征为零值、人体特征为非零值的相似节点中的人体特征偏离人体聚类中心值的节点。具体地，在一个实施例中，可以计算所述第一相似节点集合中人体特征的第一人体聚类中心值、所述第二相似节点集合中人体特征的第二人体聚类中心值。然后，可以从所述至少一个相似节点中筛选出至少一个人体特征节点，所述人体特征节点中的人脸特征为零值、人体特征为非零值。分别计算所述至少一个人体特征节点中人体特征与所述第一人体聚类中心值之间的第一人体相似度、与所述第二人体聚类中心值之间的第二人体相似度。由于第二相似节点集合中的人脸特征远偏离所述人脸特征聚类中心值，因此，所述第二相似节点集合为即将过滤掉的节点集合。若所述第二人体相似度大于所述第一人体相似度，则表示该人体特征也偏离所述目标行人的人体特征。因此，可以将所述第二人体相似度大于所述第一人体相似度时所对应的人体特征节点添加至所述第二相似节点集合中。此后，可以从所述至少一个相似节点中清除所述第二相似节点集合。
需要说明的是，在实际应用场景中，往往利用多个目标行人图像进行特征搜索，在此过程中，可以分别对所述多个目标行人图像进行特征搜索，并分别得到至少一个目标节点。最后，可以将分别得到的至少一个目标节点进行合并，并将合并之后的至少一个目标节点对应的行人图像作为所述目标行人的图像。
在本公开的一个实施例中，在获取到所述目标行人的图像之后，可以基于所述目标行人的图像，获取所述目标行人的行动轨迹，所述行动轨迹包括时间信息和/或位置信息。在一个示例中，目标行人的所述行动轨迹例如包括：2018年10月1日10:30：苏州市观 前街→2018年10月1日11:03：苏州市观前街→2018年10月1日12:50：苏州市XX停车场→……→2018年10月1日21:37：苏州市XX小区。基于以上的行动轨迹，可以获取所述目标行人的日常活动，对于公安、心理分析领域具有重要的价值。
当然，为了使得所述特征数据库包含尽可能多的数据，可以对所述特征数据库进行更新。在一个示例中，当获取到某个街道的监控视频之后，可以提取所述监控视频中的图像帧。然后，可以对所述图像帧进行特征提取，提取出所述图像帧的图像特征，所述图像特征包括人脸特征和人体特征。再将所述图像帧中的图像特征作为新的行人特征节点，更新至所述特征数据库中。
本公开各个实施例提供的行人识别方法，可以基于人脸特征和人体特征联合检索的方式从特征数据库中搜索出目标行人的图像。一方面，基于人脸特征和人体特征联合检索的方式，可以即利用了人脸特征的唯一性优势，也利用了在人脸被遮挡、人脸模糊等特殊情况下人体特征的识别优势。另一方面，所述特征数据库可以包括所述行人特征节点与其他行人特征节点之间的关系特征，这样，可以通过其中一个行人特征节点搜索到与之有关联关系的行人特征节点。基于此，可以大大降低行人搜索的计算量，提高搜索效率。
本公开实施例另一方面还提出一种行人识别装置，图3示出根据本公开实施例的行人识别装置的框图，如图3所示，所述装置300包括：
图像特征获取模块301，用于获取目标行人图像的图像特征，所述图像特征包括人脸特征和人体特征；
目标节点获取模块303，用于从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像；
其中，所述特征数据库中包括多个行人特征节点，所述行人特征节点中包括行人图像对应的人脸特征、人体特征以及与其他行人特征节点之间的关系特征。
可选的，在本公开的一个实施例中，所述关系特征被设置为根据下述参数确定：人脸图像质量值、人体图像质量值、人脸特征、人体特征。
可选的，在本公开的一个实施例中，所述关系特征包括相似节点关联关系，所述相似节点关联关系被设置为按照下述方式确定：
在两个行人特征节点中较小的人脸图像质量值大于等于预设人脸图像质量阈值的情况下，确定所述两个行人特征节点的人脸特征之间的相似度；
在所述人脸特征之间的相似度大于等于预设人脸相似度阈值的情况下，确定所述两个行人特征节点为相似节点关联关系；
在所述两个行人特征节点中较小的人脸图像质量值小于预设人脸图像质量阈值，且所述两个行人特征节点中较小的人体图像质量值大于等于人体图像质量阈值的情况下，确定所述两个行人特征节点的人体特征之间的相似度；
在所述人体特征之间的相似度大于等于预设人体相似度阈值的情况下，确定所述两个行人特征节点为相似节点关联关系。
可选的，在本公开的一个实施例中，所述目标节点获取模块包括：
路径确定子模块，用于将所述图像特征作为目标特征节点，确定所述目标特征节点到达所述行人特征节点的至少一条搜索路径，所述搜索路径由具有所述相似节点关联关系的多个行人特征节点连接而成；
路径分值确定子模块，用于确定所述搜索路径中相邻两个行人特征节点之间的相似度中的最小值，并将所述最小值作为所述搜索路径的路径分值；
节点相似度确定子模块，用于确定所述至少一条搜索路径的路径分值中的最大值，并将所述最大值作为所述目标特征节点与所述行人特征节点的相似度；
目标节点确定子模块，用于将与所述目标特征节点的相似度大于等于所述预设人脸相似度阈值或者所述预设人体相似度阈值的至少一个行人特征节点作为所述目标特征节点的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
可选的，在本公开的一个实施例中，所述目标节点获取模块包括：
相似节点搜索子模块，用于基于所述多个行人特征节点的关系特征，从所述特征数据库中搜索出所述图像特征的至少一个相似节点；
目标节点选取子模块，用于从所述至少一个相似节点中选择出至少一个目标节点；
行人图像获取子模块，用于将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
可选的，在本公开的一个实施例中，所述目标节点选取子模块包括：
人脸中心值确定单元，用于确定所述至少一个相似节点中人脸特征的人脸聚类中心值；
节点筛选单元，用于从所述至少一个相似节点中筛选出至少一个人脸人体特征节点，所述人脸人体特征节点中的人脸特征和人体特征为非零值；
节点划分单元，用于分别确定所述至少一个人脸人体特征节点中人脸特征与所述人脸聚类中心值之间的人脸相似度，将所述人脸相似度大于等于预设相似度阈值的节点划分至第一相似节点集合，将所述人脸相似度小于所述预设相似度阈值的节点划分至第二相似节点集合；
节点清除单元，用于从所述至少一个相似节点中清除所述第二相似节点集合，并将清除后的所述至少一个相似节点分别对应的行人图像作为所述目标行人的图像。
可选的，在本公开的一个实施例中，所述目标节点选取子模块还包括：
人体中心值确定单元，用于确定所述第一相似节点集合中人体特征的第一人体聚类中心值、所述第二相似节点集合中人体特征的第二人体聚类中心值；
人体节点筛选单元，用于从所述至少一个相似节点中筛选出至少一个人体特征节点，所述人体特征节点中的人脸特征为零值、人体特征为非零值；
相似度确定单元，用于分别确定所述至少一个人体特征节点中人体特征与所述第一人体聚类中心值之间的第一人体相似度、与所述第二人体聚类中心值之间的第二人体相似度；
节点添加单元，用于将所述第二人体相似度大于所述第一人体相似度时所对应的人体特征节点添加至所述第二相似节点集合中。
可选的，在本公开的一个实施例中，所述装置还包括：
行人轨迹获取模块，用于基于所述目标行人的图像，获取所述目标行人的行动轨迹，所述行动轨迹包括时间信息和/或位置信息。
可选的，在本公开的一个实施例中，所述装置还包括：
新数据获取模块，用于在获取到新行人图像的情况下，提取所述新行人图像的图像特征；
数据更新模块，用于将所述新行人图像的图像特征作为新的行人特征节点，更新至所述特征数据库中。
本公开实施例还提出一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为上述各个实施例所述的方法。
所述电子设备可以被提供为终端、服务器或其它形态的设备。
图4是根据一示例性实施例示出的一种电子设备800的框图。例如，电子设备800可以是移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗 设备，健身设备，个人数字助理等终端。
参照图4，电子设备800可以包括以下一个或多个组件：处理组件802，存储器804，电源组件806，多媒体组件808，音频组件810，输入/输出(I/O)的接口812，传感器组件814，以及通信组件816。
处理组件802通常控制电子设备800的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理组件802可以包括一个或多个处理器820来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件802可以包括一个或多个模块，便于处理组件802和其他组件之间的交互。例如，处理组件802可以包括多媒体模块，以方便多媒体组件808和处理组件802之间的交互。
存储器804被配置为存储各种类型的数据以支持在电子设备800的操作。这些数据的示例包括用于在电子设备800上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器804可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电源组件806为电子设备800的各种组件提供电力。电源组件806可以包括电源管理***，一个或多个电源，及其他与为电子设备800生成、管理和分配电力相关联的组件。
多媒体组件808包括在所述电子设备800和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件808包括一个前置摄像头和/或后置摄像头。当电子设备800处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜***或具有焦距和光学变焦能力。
音频组件810被配置为输出和/或输入音频信号。例如，音频组件810包括一个麦克风(MIC)，当电子设备800处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器804或经由通信组件816发送。在一些实施例中，音频组件810还包括一个扬声器，用于输出音频信号。
I/O接口812为处理组件802和***接口模块之间提供接口，上述***接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件814包括一个或多个传感器，用于为电子设备800提供各个方面的状态评估。例如，传感器组件814可以检测到电子设备800的打开/关闭状态，组件的相对定位，例如所述组件为电子设备800的显示器和小键盘，传感器组件814还可以检测电子设备800或电子设备800一个组件的位置改变，用户与电子设备800接触的存在或不存在，电子设备800方位或加速/减速和电子设备800的温度变化。传感器组件814可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件814还可以包括光传感器，如CMOS或CCD图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件814还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件816被配置为便于电子设备800和其他设备之间有线或无线方式的通信。电子设备800可以接入基于通信标准的无线网络，如WiFi，2G或3G，或它们的组合。在 一个示例性实施例中，通信组件816经由广播信道接收来自外部广播管理***的广播信号或广播相关信息。在一个示例性实施例中，所述通信组件816还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，电子设备800可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器804，上述计算机程序指令可由电子设备800的处理器820执行以完成上述方法。
图5是根据一示例性实施例示出的一种电子设备1900的框图。例如，电子设备1900可以被提供为一服务器。参照图5，电子设备1900包括处理组件1922，其进一步包括一个或多个处理器，以及由存储器1932所代表的存储器资源，用于存储可由处理组件1922的执行的指令，例如应用程序。存储器1932中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件1922被配置为执行指令，以执行上述方法。
电子设备1900还可以包括一个电源组件1926被配置为执行电子设备1900的电源管理，一个有线或无线网络接口1950被配置为将电子设备1900连接到网络，和一个输入输出(I/O)接口1958。电子设备1900可以操作基于存储在存储器1932的操作***，例如Windows ServerTM，Mac OS XTM，UnixTM,LinuxTM，FreeBSDTM或类似。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器1932，上述计算机程序指令可由电子设备1900的处理组件1922执行以完成上述方法。
本公开可以是***、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本公开的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是，但不限于电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本公开操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，所述编程语言包括面向物体的编程语言—诸如 Smalltalk、C++等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本公开的各个方面。
这里参照根据本公开实施例的方法、装置(***)和计算机程序产品的流程图和/或框图描述了本公开的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本公开的多个实施例的***、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的***来实现，或者可以用专用硬件与计算机指令的组合来实现。
以上已经描述了本公开的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术的改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。

Claims (21)

  1. 一种行人识别方法，其特征在于，包括：
    获取目标行人图像的图像特征，所述图像特征包括人脸特征和人体特征；
    从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像；
    其中，所述特征数据库中包括多个行人特征节点，所述行人特征节点中包括行人图像对应的人脸特征、人体特征以及与其他行人特征节点之间的关系特征。
  2. 根据权利要求1所述的行人识别方法，其特征在于，所述关系特征被设置为根据下述参数确定：人脸图像质量值、人体图像质量值、人脸特征、人体特征。
  3. 根据权利要求2所述的行人识别方法，其特征在于，所述关系特征包括相似节点关联关系，所述相似节点关联关系被设置为按照下述方式确定：
    在两个行人特征节点中较小的人脸图像质量值大于等于预设人脸图像质量阈值的情况下，确定所述两个行人特征节点的人脸特征之间的相似度；
    在所述人脸特征之间的相似度大于等于预设人脸相似度阈值的情况下，确定所述两个行人特征节点为相似节点关联关系；
    在所述两个行人特征节点中较小的人脸图像质量值小于预设人脸图像质量阈值，且所述两个行人特征节点中较小的人体图像质量值大于等于人体图像质量阈值的情况下，确定所述两个行人特征节点的人体特征之间的相似度；
    在所述人体特征之间的相似度大于等于预设人体相似度阈值的情况下，确定所述两个行人特征节点为相似节点关联关系。
  4. 根据权利要求3所述的行人识别方法，其特征在于，所述从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像，包括：
    将所述图像特征作为目标特征节点，确定所述目标特征节点到达所述行人特征节点的至少一条搜索路径，所述搜索路径由具有所述相似节点关联关系的多个行人特征节点连接而成；
    确定所述搜索路径中相邻两个行人特征节点之间的相似度中的最小值，并将所述最小值作为所述搜索路径的路径分值；
    确定所述至少一条搜索路径的路径分值中的最大值，并将所述最大值作为所述目标特征节点与所述行人特征节点的相似度；
    将与所述目标特征节点的相似度大于等于所述预设人脸相似度阈值或者所述预设人体相似度阈值的至少一个行人特征节点作为所述目标特征节点的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
  5. 根据权利要求1-3中任一项所述的行人识别方法，其特征在于，所述从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像，包括：
    基于所述多个行人特征节点的关系特征，从所述特征数据库中搜索出所述图像特征的至少一个相似节点；
    从所述至少一个相似节点中选择出至少一个目标节点；
    将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
  6. 根据权利要求5所述的行人识别方法，其特征在于，所述从所述至少一个相似节点中选择出至少一个目标节点，包括：
    确定所述至少一个相似节点中人脸特征的人脸聚类中心值；
    从所述至少一个相似节点中筛选出至少一个人脸人体特征节点，所述人脸人体特征节点中的人脸特征和人体特征为非零值；
    分别确定所述至少一个人脸人体特征节点中人脸特征与所述人脸聚类中心值之间的人脸相似度，将所述人脸相似度大于等于预设相似度阈值的节点划分至第一相似节点集合，将所述人脸相似度小于所述预设相似度阈值的节点划分至第二相似节点集合；
    从所述至少一个相似节点中清除所述第二相似节点集合，并将清除后的所述至少一个相似节点分别对应的行人图像作为所述目标行人的图像。
  7. 根据权利要求6所述的行人识别方法，其特征在于，在所述从所述至少一个相似节点中清除所述第二相似节点集合之前，所述方法还包括：
    确定所述第一相似节点集合中人体特征的第一人体聚类中心值、所述第二相似节点集合中人体特征的第二人体聚类中心值；
    从所述至少一个相似节点中筛选出至少一个人体特征节点，所述人体特征节点中的人脸特征为零值、人体特征为非零值；
    分别确定所述至少一个人体特征节点中人体特征与所述第一人体聚类中心值之间的第一人体相似度、与所述第二人体聚类中心值之间的第二人体相似度；
    将所述第二人体相似度大于所述第一人体相似度时所对应的人体特征节点添加至所述第二相似节点集合中。
  8. 根据权利要求1-7中任一项所述的行人识别方法，其特征在于，所述方法还包括：
    基于所述目标行人的图像，获取所述目标行人的行动轨迹，所述行动轨迹包括时间信息和/或位置信息。
  9. 根据权利要求1-8中任一项所述的行人识别方法，其特征在于，所述方法还包括：
    在获取到新行人图像的情况下，提取所述新行人图像的图像特征；
    将所述新行人图像的图像特征作为新的行人特征节点，更新至所述特征数据库中。
  10. 一种行人识别装置，其特征在于，包括：
    图像特征获取模块，用于获取目标行人图像的图像特征，所述图像特征包括人脸特征和人体特征；
    目标节点获取模块，用于从特征数据库中获取所述图像特征的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像；
    其中，所述特征数据库中包括多个行人特征节点，所述行人特征节点中包括行人图像对应的人脸特征、人体特征以及与其他行人特征节点之间的关系特征。
  11. 根据权利要求10所述的行人识别装置，其特征在于，所述关系特征被设置为根据下述参数确定：人脸图像质量值、人体图像质量值、人脸特征、人体特征。
  12. 根据权利要求11所述的行人识别装置，其特征在于，所述关系特征包括相似节点关联关系，所述相似节点关联关系被设置为按照下述方式确定：
    在两个行人特征节点中较小的人脸图像质量值大于等于预设人脸图像质量阈值的情况下，确定所述两个行人特征节点的人脸特征之间的相似度；
    在所述人脸特征之间的相似度大于等于预设人脸相似度阈值的情况下，确定所述两个 行人特征节点为相似节点关联关系；
    在所述两个行人特征节点中较小的人脸图像质量值小于预设人脸图像质量阈值，且所述两个行人特征节点中较小的人体图像质量值大于等于人体图像质量阈值的情况下，确定所述两个行人特征节点的人体特征之间的相似度；
    在所述人体特征之间的相似度大于等于预设人体相似度阈值的情况下，确定所述两个行人特征节点为相似节点关联关系。
  13. 根据权利要求12所述的行人识别装置，其特征在于，所述目标节点获取模块包括：
    路径确定子模块，用于将所述图像特征作为目标特征节点，确定所述目标特征节点到达所述行人特征节点的至少一条搜索路径，所述搜索路径由具有所述相似节点关联关系的多个行人特征节点连接而成；
    路径分值确定子模块，用于确定所述搜索路径中相邻两个行人特征节点之间的相似度中的最小值，并将所述最小值作为所述搜索路径的路径分值；
    节点相似度确定子模块，用于确定所述至少一条搜索路径的路径分值中的最大值，并将所述最大值作为所述目标特征节点与所述行人特征节点的相似度；
    目标节点确定子模块，用于将与所述目标特征节点的相似度大于等于所述预设人脸相似度阈值或者所述预设人体相似度阈值的至少一个行人特征节点作为所述目标特征节点的至少一个目标节点，并将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
  14. 根据权利要求10-12中任一项所述的行人识别装置，其特征在于，所述目标节点获取模块包括：
    相似节点搜索子模块，用于基于所述多个行人特征节点的关系特征，从所述特征数据库中搜索出所述图像特征的至少一个相似节点；
    目标节点选取子模块，用于从所述至少一个相似节点中选择出至少一个目标节点；
    行人图像获取子模块，用于将所述至少一个目标节点分别对应的行人图像作为所述目标行人的图像。
  15. 根据权利要求14所述的行人识别装置，其特征在于，所述目标节点选取子模块包括：
    人脸中心值确定单元，用于确定所述至少一个相似节点中人脸特征的人脸聚类中心值；
    节点筛选单元，用于从所述至少一个相似节点中筛选出至少一个人脸人体特征节点，所述人脸人体特征节点中的人脸特征和人体特征为非零值；
    节点划分单元，用于分别确定所述至少一个人脸人体特征节点中人脸特征与所述人脸聚类中心值之间的人脸相似度，将所述人脸相似度大于等于预设相似度阈值的节点划分至第一相似节点集合，将所述人脸相似度小于所述预设相似度阈值的节点划分至第二相似节点集合；
    节点清除单元，用于从所述至少一个相似节点中清除所述第二相似节点集合，并将清除后的所述至少一个相似节点分别对应的行人图像作为所述目标行人的图像。
  16. 根据权利要求15所述的行人识别装置，其特征在于，所述目标节点选取子模块还包括：
    人体中心值确定单元，用于确定所述第一相似节点集合中人体特征的第一人体聚类中 心值、所述第二相似节点集合中人体特征的第二人体聚类中心值；
    人体节点筛选单元，用于从所述至少一个相似节点中筛选出至少一个人体特征节点，所述人体特征节点中的人脸特征为零值、人体特征为非零值；
    相似度确定单元，用于分别确定所述至少一个人体特征节点中人体特征与所述第一人体聚类中心值之间的第一人体相似度、与所述第二人体聚类中心值之间的第二人体相似度；
    节点添加单元，用于将所述第二人体相似度大于所述第一人体相似度时所对应的人体特征节点添加至所述第二相似节点集合中。
  17. 根据权利要求10-16中任一项所述的行人识别装置，其特征在于，所述装置还包括：
    行人轨迹获取模块，用于基于所述目标行人的图像，获取所述目标行人的行动轨迹，所述行动轨迹包括时间信息和/或位置信息。
  18. 根据权利要求10-17中任一项所述的行人识别装置，其特征在于，所述装置还包括：
    新数据获取模块，用于在获取到新行人图像的情况下，提取所述新行人图像的图像特征；
    数据更新模块，用于将所述新行人图像的图像特征作为新的行人特征节点，更新至所述特征数据库中。
  19. 一种电子设备，其特征在于，包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为执行权利要求1-9任意一项所述的行人识别方法。
  20. 一种非临时性计算机可读存储介质，当所述存储介质中的指令由处理器执行时，使得处理器能够执行权利要求1-9任意一项所述的行人识别方法。
  21. 一种计算机程序，其特征在于，所述计算机程序包括计算机可读代码，当所述计算机可读代码在电子设备中运行时，所述电子设备中的处理器执行用于实现权利要求1至9中的任意一项所述的方法。
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