CN115187915A - Passenger flow analysis method, device, equipment and medium - Google Patents
Passenger flow analysis method, device, equipment and medium Download PDFInfo
- Publication number
- CN115187915A CN115187915A CN202211086692.7A CN202211086692A CN115187915A CN 115187915 A CN115187915 A CN 115187915A CN 202211086692 A CN202211086692 A CN 202211086692A CN 115187915 A CN115187915 A CN 115187915A
- Authority
- CN
- China
- Prior art keywords
- feature
- target
- characteristic
- passenger flow
- database
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/10—Image acquisition
- G06V10/12—Details of acquisition arrangements; Constructional details thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
-
- 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
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/761—Proximity, similarity or dissimilarity measures
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The application discloses passenger flow analysis method, device, equipment and medium, relates to the technical field of passenger flow statistics, is applied to target image acquisition equipment, and the target image acquisition equipment is any image acquisition equipment in a local area network, and comprises the following steps: collecting video stream data comprising a target client, and screening a target frame image from the video stream data according to a preset condition; extracting features of the target frame image to obtain corresponding feature values, and performing feature search from feature databases corresponding to image acquisition devices in the local area network to determine whether target feature values matched with the feature values exist in the feature databases; and acquiring a characteristic search result, and reporting a corresponding passenger flow analysis result to a server based on the characteristic search result. According to the method and the device, mutual identification between the characteristic databases corresponding to the image acquisition devices in the local area network is realized, namely, data sharing among different devices is realized, so that the accuracy of passenger flow data analysis is improved.
Description
Technical Field
The invention relates to the technical field of passenger flow statistics, in particular to a passenger flow analysis method, a passenger flow analysis device, passenger flow analysis equipment and a passenger flow analysis medium.
Background
In a multi-device scenario, for example, a store has multiple entrances and exits, and each entrance and exit is equipped with an image acquisition device, specifically a camera. However, data isolation among the image acquisition devices cannot realize data sharing, so that the accuracy of passenger flow analysis is influenced. The traditional method is that image data acquired by each image acquisition device is synchronized to a cloud, a large database is maintained through the cloud, and each device is identified through the cloud, so that the purpose of data sharing is achieved. However, the method has the problems of frequent data exchange, occupation of server resources, low identification efficiency and the like.
In summary, how to improve the accuracy of passenger flow data analysis and reduce the occupation of server resources is a problem to be solved at present.
Disclosure of Invention
In view of this, the present invention provides a method, an apparatus, a device, and a medium for analyzing passenger flow, which can improve accuracy of analyzing passenger flow data and reduce occupation of server resources. The specific scheme is as follows:
in a first aspect, the present application discloses a passenger flow analysis method, which is applied to a target image acquisition device, where the target image acquisition device is any one image acquisition device in a local area network, and the method includes:
collecting video stream data comprising a target client, and screening a target frame image from the video stream data according to a preset condition;
extracting features of the target frame image to obtain corresponding feature values, and performing feature search from feature databases corresponding to image acquisition devices in the local area network to determine whether target feature values matched with the feature values exist in the feature databases;
and acquiring a characteristic search result, and reporting a corresponding passenger flow analysis result to a server based on the characteristic search result.
Optionally, the performing feature extraction on the target frame image to obtain a corresponding feature value includes:
and performing feature extraction on the target frame image according to the shape type to obtain a corresponding shape feature value.
Optionally, performing feature search from feature databases corresponding to the image acquisition devices in the local area network to determine whether a target feature value matching the feature value exists in each feature database includes:
determining a first feature database corresponding to the target image acquisition equipment, and performing feature search from the first feature database to determine whether a target feature value matched with the feature value exists in the first feature database;
if the target characteristic value matched with the characteristic value does not exist in the first characteristic database, determining a second characteristic database corresponding to other image acquisition devices in the local area network, and initiating a characteristic search request to the second characteristic database to determine whether the target characteristic value matched with the characteristic value exists in the second characteristic database.
Optionally, after determining whether a target feature value matching the feature value exists in the first feature database, the method further includes:
and if the first characteristic database has a target characteristic value matched with the characteristic value, judging that the target client is mature client flow, and reporting a corresponding client flow analysis result to a server.
Optionally, the obtaining the feature search result and reporting the corresponding passenger flow analysis result to the server based on the feature search result includes:
obtaining feature search results corresponding to the feature search requests, returned by the other image acquisition devices;
and if the characteristic search result is that the second characteristic database has a target characteristic value matched with the characteristic value, judging that the target client is mature client flow, and reporting a corresponding client flow judgment result to a server.
Optionally, after obtaining feature search results corresponding to the feature search request and returned by the other image acquisition devices, the method further includes:
if the characteristic search result is that the second characteristic database does not have a target characteristic value matched with the characteristic value, storing the characteristic value into the first characteristic database;
and judging that the target client is a new client flow, and reporting a corresponding client flow judgment result to a server.
Optionally, the determining whether a target feature value matching the feature value exists in each of the feature databases includes:
calculating a similarity score between each feature data in each feature database and the feature value, and judging whether the similarity score exceeds a preset score threshold value;
and if the similarity score exceeds a preset score threshold value, determining that a target characteristic value matched with the characteristic value exists in the characteristic database.
In a second aspect, the present application discloses a passenger flow analysis device, which is applied to a target image capturing device, wherein the target image capturing device is any one image capturing device in a local area network, and the passenger flow analysis device includes:
the target frame image screening module is used for acquiring video stream data comprising target clients and screening target frame images from the video stream data according to preset conditions;
the characteristic searching module is used for extracting the characteristics of the target frame image to obtain corresponding characteristic values and searching the characteristics from a characteristic database corresponding to each image acquisition device in the local area network to determine whether a target characteristic value matched with the characteristic value exists in each characteristic database;
and the result reporting module is used for acquiring the characteristic search result and reporting a corresponding passenger flow analysis result to the server based on the characteristic search result.
In a third aspect, the present application discloses an electronic device, comprising:
a memory for storing a computer program;
a processor for executing said computer program for implementing the steps of the aforementioned disclosed passenger flow analysis method.
In a fourth aspect, the present application discloses a computer readable storage medium for storing a computer program; wherein the computer program realizes the steps of the aforementioned disclosed passenger flow analysis method when being executed by a processor.
Therefore, the method comprises the steps that video stream data including a target client are collected through target image collecting equipment, and a target frame image is screened from the video stream data according to preset conditions; extracting features of the target frame image to obtain corresponding feature values, and performing feature search from feature databases corresponding to image acquisition devices in the local area network to determine whether target feature values matched with the feature values exist in the feature databases; and acquiring a characteristic search result, and reporting a corresponding passenger flow analysis result to a server based on the characteristic search result. Therefore, the method comprises the steps of firstly acquiring video stream data comprising a target client through target image acquisition equipment, screening a target frame image from the video stream data according to preset conditions, then performing feature extraction on the target frame image to obtain corresponding feature values, then determining feature databases respectively corresponding to the image acquisition equipment in a local area network, so as to perform feature search from the feature databases based on the feature values to determine whether target feature values matched with the feature values exist in the feature databases or not, and then reporting passenger flow analysis results to a server according to corresponding feature search results. Through the scheme, mutual identification among the feature databases corresponding to the image acquisition devices in the local area network is realized, namely, data sharing among different devices is realized, so that the accuracy of passenger flow data analysis is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a passenger flow analysis method disclosed herein;
FIG. 2 is a flow chart of a specific passenger flow analysis method disclosed herein;
FIG. 3 is a schematic view of a specific passenger flow analysis scenario disclosed herein;
FIG. 4 is a schematic view of a specific passenger flow analysis process disclosed herein;
fig. 5 is a schematic structural view of a passenger flow analysis device disclosed in the present application;
fig. 6 is a block diagram of an electronic device disclosed in the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, the conventional method is to synchronize image data acquired by each image acquisition device to a cloud end, maintain a large database through the cloud end, and identify each device through the cloud end, so as to achieve the purpose of data sharing. However, the method has the problems of frequent data exchange, occupation of server resources, low identification efficiency and the like. Therefore, the embodiment of the application discloses a passenger flow analysis method, a passenger flow analysis device, passenger flow analysis equipment and a passenger flow analysis medium, which can improve the accuracy of passenger flow data analysis and reduce the occupation of server resources.
Referring to fig. 1, an embodiment of the present application discloses a passenger flow analysis method, which is applied to a target image capturing device, where the target image capturing device is any one image capturing device in a local area network, and the method includes:
step S11: the method comprises the steps of collecting video stream data comprising a target client, and screening a target frame image from the video stream data according to a preset condition.
In this embodiment, video stream data including a target client is collected by a target image collecting device, and a target frame image is screened from the video stream data according to a preset condition. In a specific embodiment, the image capturing device may be a camera, and the video stream data may be video stream data captured when the target client enters a store, so that the target frame image is a front image of the target client, and is an optimal and clearest frame image selected from the video stream data according to the posture information and the angle information of the target client.
Step S12: and extracting features of the target frame image to obtain corresponding feature values, and performing feature search from feature databases corresponding to image acquisition devices in the local area network to determine whether target feature values matched with the feature values exist in the feature databases.
In this embodiment, feature extraction is performed on the screened target frame image to obtain a corresponding feature value. In a specific embodiment, the extracting the features of the target frame image to obtain the corresponding feature values includes: and performing feature extraction on the target frame image according to the shape type to obtain a corresponding shape feature value. That is, the content of feature extraction in the present application may be a body shape, and besides, may also include, but is not limited to, feature extraction on information such as gender and age. It should be noted that, in the embodiment of the present application, it is not necessary to transmit the video image data acquired by the target image acquisition device to a specific recognition device for feature extraction, but the target image acquisition device directly performs feature extraction, that is, the image acquisition device may be understood as a passenger flow analyzer, and can directly perform passenger flow analysis on the video image data.
Further, the determining whether a target feature value matching the feature value exists in each of the feature databases includes: calculating a similarity score between each feature data in each feature database and the feature value, and judging whether the similarity score exceeds a preset score threshold value; and if the similarity score exceeds a preset score threshold value, determining that a target characteristic value matched with the characteristic value exists in the characteristic database. It can be understood that, in this embodiment, it is necessary to determine whether a target feature value matching the feature value exists in each feature database, and perform feature search on the extracted feature values in each feature database in sequence to determine whether the feature value hits in a certain feature database, where if the feature value hits, it means that feature data information in at least one feature database matches the feature value. Specifically, by calculating a similarity score between each feature data and a feature value in each feature database and setting a preset score threshold, if the calculated similarity score exceeds the preset score threshold, it can be considered that a target feature value matching the feature value exists in the feature database, that is, the feature value is hit in a certain feature database. It should be noted that in this embodiment, there are at least two image capturing devices in the local area network, and each image capturing device has its own corresponding feature database. Then, assuming that the first image acquisition device extracts the body shape characteristic value of the target user currently, then, calculating a similarity score between each characteristic data in the two characteristic databases and the body shape characteristic value, and setting a preset score threshold value to be 80, when the similarity score exceeds 80, determining that the target characteristic value matched with the body shape characteristic value exists in the corresponding characteristic database.
Step S13: and acquiring a characteristic search result, and reporting a corresponding passenger flow analysis result to a server based on the characteristic search result.
In this embodiment, a corresponding feature search result, that is, a target feature value corresponding to the feature value exists or does not exist in each feature database, is obtained, and a passenger flow analysis result is reported to the server based on the feature search result. It can be understood that the passenger flow analysis result may be of two types, that is, the target client is an aged passenger flow or a new passenger flow, where the aged passenger flow may refer to a passenger flow whose relevant characteristics are recorded in the image acquisition device, that is, if a target characteristic value corresponding to the characteristic value exists in the characteristic database corresponding to each image acquisition device, the target client is considered to be an aged passenger flow; the new passenger flow may refer to a passenger flow in which relevant features are not recorded in the image acquisition device, that is, if a target feature value corresponding to the feature value does not exist in the feature database corresponding to each image acquisition device, the target client may be considered as a new passenger flow.
Therefore, the method comprises the steps that video stream data including target clients are collected through target image collecting equipment, and target frame images are screened out from the video stream data according to preset conditions; extracting features of the target frame image to obtain corresponding feature values, and performing feature search from feature databases corresponding to image acquisition devices in the local area network to determine whether target feature values matched with the feature values exist in the feature databases; and acquiring a characteristic search result, and reporting a corresponding passenger flow analysis result to a server based on the characteristic search result. Therefore, the method comprises the steps of firstly acquiring video stream data comprising a target client through target image acquisition equipment, screening a target frame image from the video stream data according to preset conditions, then performing feature extraction on the target frame image to obtain corresponding feature values, then determining feature databases respectively corresponding to the image acquisition equipment in a local area network, so as to perform feature search from the feature databases based on the feature values to determine whether target feature values matched with the feature values exist in the feature databases or not, and then reporting passenger flow analysis results to a server according to corresponding feature search results. Through the scheme, mutual identification among the feature databases corresponding to the image acquisition devices in the local area network is realized, namely, data sharing among different devices is realized, so that the accuracy of passenger flow data analysis is improved.
Referring to fig. 2, the embodiment of the present application discloses a specific passenger flow analysis method, and compared with the previous embodiment, the present embodiment further describes and optimizes the technical solution. The method specifically comprises the following steps:
step S21: the method comprises the steps of collecting video stream data comprising a target client, and screening a target frame image from the video stream data according to a preset condition.
Step S22: and extracting the features of the target frame image to obtain a corresponding feature value, determining a first feature database corresponding to the target image acquisition equipment, and performing feature search from the first feature database to determine whether a target feature value matched with the feature value exists in the first feature database.
In this embodiment, a Resnet50 algorithm may be specifically used to perform feature extraction on the target frame image to obtain a corresponding feature value, and then, when performing feature search from a feature database corresponding to each image acquisition device based on the feature value, feature search is preferentially performed from the first feature database of the target image acquisition device itself to determine whether a target feature value matching the feature value exists in the first feature database.
Step S23: if the target characteristic value matched with the characteristic value does not exist in the first characteristic database, determining a second characteristic database corresponding to other image acquisition devices in the local area network, and initiating a characteristic search request to the second characteristic database to determine whether the target characteristic value matched with the characteristic value exists in the second characteristic database.
In a specific embodiment, if the first feature database does not have a target feature value matching the feature value, then alternately initiating a feature search request to second feature databases corresponding to other image acquisition devices in the local area network to determine whether the second feature database has the target feature value matching the feature value. It should be noted that, when performing feature search on each second feature database, the search may be performed sequentially according to the identification number of the image capturing device, or may be performed randomly, which is not limited in this embodiment. It can be understood that the target image acquisition device can perform bidirectional data transmission, in the above process, the target image acquisition device can be understood as a client, and the rest of image acquisition devices in the local area network are used as servers, that is, the client initiates a feature search request to the server, and then judges whether features are matched according to the response of the server; meanwhile, the target image acquisition equipment can also be used as a server to obtain the feature search requests sent by other image acquisition equipment, then perform feature search from the first feature database of the equipment, and return corresponding matching results. That is, through the local area network communication technology, each image acquisition device is a client and a server, shared identification of the feature library, namely feature shared identification, among multiple devices can be achieved, and server information does not need to be requested, so that occupation of server resources is reduced, and identification efficiency is improved. In addition, besides a plurality of image acquisition devices are deployed in the local area network, a switch is also deployed, and the image acquisition devices and the switch jointly form a passenger flow analysis system. When the client device sends the feature search request to the server device, the client device needs to send the feature search request to a switch in the local area network, and then the switch forwards the feature search request to the server device; similarly, when the server device returns the matching result, the server device also forwards the matching result through the switch. By switch is meant a "switch" which is a network device for electrical/optical signal forwarding. It may provide an exclusive electrical signal path for any two network nodes accessing the switch.
In another specific embodiment, after determining whether the target feature value matching the feature value exists in the first feature database, the method further includes: and if the first characteristic database has a target characteristic value matched with the characteristic value, judging that the target client is mature client flow, and reporting a corresponding client flow analysis result to a server. It will be appreciated that the feature search is stopped if there is a target feature value in the first feature database that matches the feature value, i.e. the feature value hits within the first feature data. At this time, the target client can be judged to be the frequent passenger flow, and the passenger flow analysis result of the target client which is the frequent passenger flow is reported to the server.
Step S24: and obtaining feature search results corresponding to the feature search requests returned by the other image acquisition devices.
In this embodiment, after acquiring the feature search request sent by the current target image acquisition device, the other image acquisition devices perform feature search in the respective corresponding second feature databases, and return corresponding feature search results.
Step S25: and if the characteristic search result is that the second characteristic database has a target characteristic value matched with the characteristic value, judging that the target client is mature client flow, and reporting a corresponding client flow judgment result to a server.
In this embodiment, if the feature search result is that the second feature database has a target feature value matching the feature value, it may be determined that the target client is a frequent passenger flow, and a passenger flow analysis result that the target client is a frequent passenger flow is reported to the server. It should be noted that, as long as the target feature value matching the feature value is searched in any one of the second feature databases, that is, the feature value hits in any one of the second feature databases, the feature search is stopped, and the feature search result is returned.
Further, after obtaining feature search results corresponding to the feature search request returned by the remaining image capturing devices, the method further includes: if the characteristic search result is that the second characteristic database does not have a target characteristic value matched with the characteristic value, storing the characteristic value into the first characteristic database; and judging that the target client is a new client flow, and reporting a corresponding client flow judgment result to a server. That is, if the second feature databases corresponding to the image capturing devices do not have a target feature value matching the feature value, in other words, the feature value is not hit in the second feature databases, the feature value is stored in the first feature database of the device, the target client is determined to be a new passenger flow, and a passenger flow determination result that the target client is a new passenger flow is reported to the server.
For a more specific processing procedure of the step S21, reference may be made to corresponding contents disclosed in the foregoing embodiments, and details are not repeated here.
In the embodiment of the application, when feature search is performed from the feature database corresponding to each image acquisition device based on the extracted feature values, feature search is preferentially performed from the first feature database of the target image acquisition device, and if a matched target feature value exists in the first feature database, the frequent visitor flow is reported to the server; if the matched target characteristic value exists in the first characteristic database, then alternately initiating a characteristic search request to second characteristic databases corresponding to other image acquisition devices in the local area network, and if the matched target characteristic value exists in any one of the second characteristic databases, reporting the frequent passenger flow to a server; and if the matched target characteristic values do not exist in all the second characteristic databases, storing the characteristic values into the first characteristic database of the equipment, and reporting the new passenger flow to the server. In addition, each image acquisition device can serve as a client to send feature search requests to other image acquisition devices, and can also serve as a server to obtain the feature search requests sent by other devices, namely shared identification of a feature library can be completed among multiple devices, database synchronization is not needed through a cloud server, server resources are not needed to be occupied, data exchange is reduced, and identification efficiency is improved.
The following describes the technical solution in the present application by taking the specific scenario shown in fig. 3 as an example.
As shown in fig. 3, a store has three doors, each equipped with a camera, camera 1, camera 2 and camera 3. As shown in fig. 4, it is assumed that a target client enters a store from a door corresponding to the camera 1, at this time, the camera 1 acquires video stream data including the target client, and then the camera 1 performs feature extraction on a target frame image in the video stream data to obtain a feature value. And then according to the characteristic value, preferentially performing characteristic search from a characteristic database corresponding to the camera 1 to determine whether a target characteristic value matched with the characteristic value exists, and if not, performing characteristic search from characteristic databases corresponding to the camera 2 and the camera 3. In the process, once the characteristic value is hit in any one characteristic database, the subsequent characteristic search is stopped, and the frequent visitor flow is reported to a server; if the three feature databases are not hit, the feature value is stored in the feature database of the camera 1, and new passenger flow is reported to the server.
Referring to fig. 5, an embodiment of the present application discloses a passenger flow analysis apparatus, which is applied to a target image capturing device, where the target image capturing device is any one image capturing device in a local area network, and the apparatus includes:
the target frame image screening module 11 is configured to collect video stream data including a target client, and screen a target frame image from the video stream data according to a preset condition;
the feature searching module 12 is configured to perform feature extraction on the target frame image to obtain a corresponding feature value, and perform feature search from a feature database corresponding to each image acquisition device in the local area network to determine whether a target feature value matching the feature value exists in each feature database;
and a result reporting module 13, configured to obtain the feature search result, and report a corresponding passenger flow analysis result to the server based on the feature search result.
Therefore, the method comprises the steps that video stream data including a target client are collected through target image collecting equipment, and a target frame image is screened from the video stream data according to preset conditions; extracting features of the target frame image to obtain corresponding feature values, and performing feature search from feature databases corresponding to image acquisition devices in the local area network to determine whether target feature values matched with the feature values exist in the feature databases; and acquiring a characteristic search result, and reporting a corresponding passenger flow analysis result to a server based on the characteristic search result. Therefore, the method comprises the steps of firstly acquiring video stream data comprising a target client through target image acquisition equipment, screening a target frame image from the video stream data according to preset conditions, then performing feature extraction on the target frame image to obtain corresponding feature values, then determining feature databases respectively corresponding to the image acquisition equipment in a local area network, so as to perform feature search from the feature databases based on the feature values to determine whether target feature values matched with the feature values exist in the feature databases or not, and then reporting passenger flow analysis results to a server according to corresponding feature search results. Through the scheme, mutual identification among the feature databases corresponding to the image acquisition devices in the local area network is realized, namely, data sharing among different devices is realized, so that the accuracy of passenger flow data analysis is improved.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The method specifically comprises the following steps: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. The memory 22 is configured to store a computer program, and the computer program is loaded and executed by the processor 21 to implement relevant steps in the passenger flow analysis method executed by an electronic device disclosed in any of the foregoing embodiments.
In this embodiment, the power supply 23 is configured to provide a working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and a communication protocol followed by the communication interface is any communication protocol applicable to the technical solution of the present application, and is not specifically limited herein; the input/output interface 25 is configured to acquire external input data or output data to the outside, and a specific interface type thereof may be selected according to specific application requirements, which is not specifically limited herein.
The processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 21 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, the processor 21 may further include an AI (Artificial Intelligence) processor for processing a calculation operation related to machine learning.
In addition, the storage 22 is used as a carrier for storing resources, and may be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., the resources stored thereon include an operating system 221, a computer program 222, data 223, etc., and the storage may be a transient storage or a permanent storage.
The operating system 221 is used for managing and controlling each hardware device on the electronic device 20 and the computer program 222, so as to implement the operation and processing of the mass data 223 in the memory 22 by the processor 21, which may be Windows, unix, linux, or the like. The computer program 222 may further include a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the passenger flow analysis method performed by the electronic device 20 disclosed in any of the foregoing embodiments. The data 223 may include data received by the electronic device and transmitted from an external device, or may include data collected by the input/output interface 25 itself.
Further, an embodiment of the present application further discloses a computer-readable storage medium, in which a computer program is stored, and when the computer program is loaded and executed by a processor, the method steps executed in the passenger flow analysis process disclosed in any of the foregoing embodiments are implemented.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The method, the apparatus, the device and the storage medium for analyzing passenger flow provided by the present invention are described in detail above, and a specific example is applied in the description to explain the principle and the implementation of the present invention, and the description of the above embodiment is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A passenger flow analysis method is applied to target image acquisition equipment, wherein the target image acquisition equipment is any image acquisition equipment in a local area network, and the method comprises the following steps:
collecting video stream data comprising a target client, and screening a target frame image from the video stream data according to a preset condition;
extracting features of the target frame image to obtain corresponding feature values, and performing feature search from feature databases corresponding to image acquisition devices in the local area network to determine whether target feature values matched with the feature values exist in the feature databases;
and acquiring a characteristic search result, and reporting a corresponding passenger flow analysis result to a server based on the characteristic search result.
2. The passenger flow analysis method according to claim 1, wherein said extracting features of the target frame image to obtain corresponding feature values comprises:
and extracting the characteristics of the target frame image according to the shape type to obtain a corresponding shape characteristic value.
3. The passenger flow analysis method according to claim 1, wherein the performing a feature search from a feature database corresponding to each image capturing device in the local area network to determine whether a target feature value matching the feature value exists in each feature database comprises:
determining a first feature database corresponding to the target image acquisition equipment, and performing feature search from the first feature database to determine whether a target feature value matched with the feature value exists in the first feature database;
if the target characteristic value matched with the characteristic value does not exist in the first characteristic database, determining a second characteristic database corresponding to other image acquisition devices in the local area network, and initiating a characteristic search request to the second characteristic database to determine whether the target characteristic value matched with the characteristic value exists in the second characteristic database.
4. The passenger flow analysis method according to claim 3, wherein after determining whether there is a target feature value matching the feature value in the first feature database, further comprising:
and if the first characteristic database has a target characteristic value matched with the characteristic value, judging that the target client is mature client flow, and reporting a corresponding client flow analysis result to a server.
5. The passenger flow analysis method according to claim 3, wherein the obtaining of the feature search result and the reporting of the corresponding passenger flow analysis result to the server based on the feature search result comprises:
obtaining feature search results corresponding to the feature search requests returned by the other image acquisition devices;
and if the characteristic search result is that the second characteristic database has a target characteristic value matched with the characteristic value, judging that the target client is mature client flow, and reporting a corresponding client flow judgment result to a server.
6. The passenger flow analysis method according to claim 5, wherein after obtaining feature search results corresponding to the feature search request returned by the remaining image capturing devices, the method further comprises:
if the characteristic search result is that the second characteristic database does not have a target characteristic value matched with the characteristic value, storing the characteristic value into the first characteristic database;
and judging that the target client is a new client flow, and reporting a corresponding client flow judgment result to a server.
7. The passenger flow analysis method according to any one of claims 1 to 6, wherein said determining whether a target feature value matching the feature value exists in each of the feature databases comprises:
calculating a similarity score between each feature data in each feature database and the feature value, and judging whether the similarity score exceeds a preset score threshold value;
and if the similarity score exceeds a preset score threshold value, determining that a target characteristic value matched with the characteristic value exists in the characteristic database.
8. A passenger flow analysis device is applied to target image acquisition equipment, wherein the target image acquisition equipment is any one image acquisition equipment in a local area network, and the passenger flow analysis device comprises:
the target frame image screening module is used for acquiring video stream data comprising target clients and screening target frame images from the video stream data according to preset conditions;
the characteristic searching module is used for extracting the characteristics of the target frame image to obtain corresponding characteristic values and searching the characteristics from a characteristic database corresponding to each image acquisition device in the local area network to determine whether a target characteristic value matched with the characteristic value exists in each characteristic database;
and the result reporting module is used for acquiring the characteristic search result and reporting a corresponding passenger flow analysis result to the server based on the characteristic search result.
9. An electronic device, comprising:
a memory for storing a computer program;
processor for executing said computer program for carrying out the steps of the passenger flow analysis method according to any one of claims 1 to 7.
10. A computer-readable storage medium for storing a computer program; wherein the computer program when executed by a processor realizes the steps of the method of passenger flow analysis according to any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211086692.7A CN115187915A (en) | 2022-09-07 | 2022-09-07 | Passenger flow analysis method, device, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211086692.7A CN115187915A (en) | 2022-09-07 | 2022-09-07 | Passenger flow analysis method, device, equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115187915A true CN115187915A (en) | 2022-10-14 |
Family
ID=83524375
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211086692.7A Pending CN115187915A (en) | 2022-09-07 | 2022-09-07 | Passenger flow analysis method, device, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115187915A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105488478A (en) * | 2015-12-02 | 2016-04-13 | 深圳市商汤科技有限公司 | Face recognition system and method |
| CN110459058A (en) * | 2019-08-06 | 2019-11-15 | 北京市公安局公安交通管理局 | A kind of separated stop board recognition methods of multiple-camera collaboration and device |
| CN110688974A (en) * | 2019-09-30 | 2020-01-14 | 支付宝(杭州)信息技术有限公司 | Identity recognition method and device |
| CN111950364A (en) * | 2020-07-07 | 2020-11-17 | 北京思特奇信息技术股份有限公司 | System and method for identifying face of tens of millions of base libraries in different libraries |
| CN113177480A (en) * | 2021-04-29 | 2021-07-27 | 上海商汤智能科技有限公司 | Financial business processing method, device, equipment and medium based on face recognition |
| WO2022033068A1 (en) * | 2020-08-14 | 2022-02-17 | 华为技术有限公司 | Image management method and apparatus, and terminal device and system |
-
2022
- 2022-09-07 CN CN202211086692.7A patent/CN115187915A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105488478A (en) * | 2015-12-02 | 2016-04-13 | 深圳市商汤科技有限公司 | Face recognition system and method |
| CN110459058A (en) * | 2019-08-06 | 2019-11-15 | 北京市公安局公安交通管理局 | A kind of separated stop board recognition methods of multiple-camera collaboration and device |
| CN110688974A (en) * | 2019-09-30 | 2020-01-14 | 支付宝(杭州)信息技术有限公司 | Identity recognition method and device |
| CN111950364A (en) * | 2020-07-07 | 2020-11-17 | 北京思特奇信息技术股份有限公司 | System and method for identifying face of tens of millions of base libraries in different libraries |
| WO2022033068A1 (en) * | 2020-08-14 | 2022-02-17 | 华为技术有限公司 | Image management method and apparatus, and terminal device and system |
| CN113177480A (en) * | 2021-04-29 | 2021-07-27 | 上海商汤智能科技有限公司 | Financial business processing method, device, equipment and medium based on face recognition |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109492536B (en) | Face recognition method and system based on 5G framework | |
| CN111125409B (en) | Control method and device of access control system and access control system | |
| CN109308490B (en) | Method and apparatus for generating information | |
| CN108446692B (en) | Face comparison method, device and system | |
| CN108491773A (en) | A kind of recognition methods and system | |
| CN107092686B (en) | File management method and device based on cloud storage platform | |
| TW202101384A (en) | Image processing method, proposal evaluation method, and related devices | |
| CN110245268A (en) | A kind of route determination, the method and device of displaying | |
| US20220301317A1 (en) | Method and device for constructing object motion trajectory, and computer storage medium | |
| CN111107423A (en) | Video service playing card pause identification method and device | |
| CN109902681B (en) | User group relation determining method, device, equipment and storage medium | |
| CN111177469A (en) | Face retrieval method and face retrieval device | |
| CN111930913A (en) | Knowledge graph-based question and answer method, system, device, equipment and medium | |
| CN111753743A (en) | Face recognition method and system based on gatekeeper | |
| CN115827363A (en) | Resource alarm analysis method and device, electronic equipment and storage medium | |
| CN113691483B (en) | Method, device and equipment for detecting abnormal user equipment and storage medium | |
| CN112487082B (en) | Biological feature recognition method and related equipment | |
| JP6733766B1 (en) | Analysis device, control method, and program | |
| CN115187915A (en) | Passenger flow analysis method, device, equipment and medium | |
| CN111628905A (en) | Data packet capturing method, device and equipment | |
| CN113283410B (en) | Face enhancement recognition method, device and equipment based on data association analysis | |
| CN115909583A (en) | Intelligent passage processing system and method and electronic equipment | |
| CN113378005B (en) | Event processing method, device, electronic equipment and storage medium | |
| US20210272279A1 (en) | Image Processing Method for Displaying Cells of a Plurality of Overall Images | |
| CN114495188A (en) | Image data processing method and device and related equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |