CN111708907A - Target person query method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a storage medium for inquiring target personnel. Wherein, the method comprises the following steps: acquiring a target person image, and determining a target person area in the target person image; determining values of pixel points of the target personnel area in an RGB space, and clustering the target personnel area according to the values in the RGB space to obtain at least two value distribution families; determining the value ratio of the target personnel area according to the at least two value distribution families; and storing the value ratio into a database so as to conveniently inquire the target personnel according to the data in the database. The embodiment of the invention determines the color composition of the target personnel in the color space by acquiring the target personnel area, and queries the target personnel according to the color composition. The problem of low query precision caused by storage through the face or single color in the prior art is solved, and the query efficiency and precision of target personnel are improved.
Description
Technical Field
The embodiment of the invention relates to computer technology, in particular to a method, a device, equipment and a storage medium for querying a target person.
Background
With the increase of outdoor activities of people, people finding is a frequently encountered demand in public places such as airports, railway stations, bank business halls and the like, the time of people can be effectively saved by improving the target query precision, and the user satisfaction is improved.
In the prior art, target query is mostly carried out through equipment such as a camera, and due to the problems of arrangement positions, angles and the like of the camera, the camera is difficult to obtain clear front photos meeting the requirement of face recognition, so that the searching efficiency and precision of target personnel are low.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for querying target personnel, which are used for improving the query precision and efficiency of the target personnel.
In a first aspect, an embodiment of the present invention provides a method for querying a target person, where the method includes:
acquiring a target person image, and determining a target person area in the target person image;
determining values of pixel points of the target personnel area in an RGB space, and clustering the target personnel area according to the values in the RGB space to obtain at least two value distribution families;
determining the value ratio of the target personnel area according to the at least two value distribution families;
and storing the value ratio into a database so as to conveniently inquire the target personnel according to the data in the database.
In a second aspect, an embodiment of the present invention further provides an apparatus for querying a target person, where the apparatus includes:
the target area determining module is used for acquiring a target person image and determining a target person area in the target person image;
a value distribution family obtaining module, configured to determine values of pixel points of the target person region in an RGB space, and perform clustering processing on the target person region according to the values in the RGB space to obtain at least two value distribution families;
the proportion determining module is used for determining the value proportion of the target personnel area according to the at least two value distribution families;
and the target query module stores the value ratio into a database so as to query the target personnel according to the data in the database.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for querying a target person according to any embodiment of the present application.
In a fourth aspect, an embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the method for querying a target person according to any embodiment of the present application.
The embodiment of the invention obtains the target personnel area from the target personnel image, clusters the target personnel area to obtain a plurality of value distribution groups, determines the color value ratio of the target personnel according to the pixel values of the value distribution groups in the color space, and queries the target personnel according to the color value ratio. The problem of among the prior art carry out personnel's data storage through the face or single color, the target personnel inquiry precision is low that causes is solved, through confirming the color composition, has improved the efficiency and the precision of target personnel's inquiry.
Drawings
Fig. 1 is a flowchart illustrating a method for querying a target person according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a pedestrian frame according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for querying a target person according to a second embodiment of the present invention;
fig. 4 is a block diagram of an inquiry apparatus for a target person according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device in the fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a schematic flowchart of a method for querying a target person according to an embodiment of the present invention, where the embodiment is applicable to querying the target person, and the method can be executed by a querying device of the target person. As shown in fig. 1, the method specifically includes the following steps:
and S110, acquiring the target person image and determining the target person area in the target person image.
The target person image is an image containing a target person, and can be directly obtained from a plurality of images or extracted from a video. After the target person image is obtained, the area where the target person is located in the target person image is determined, and the area of the target person can be framed.
In this embodiment, optionally, the obtaining of the image of the target person includes: and performing frame extraction on the video data input into the target person according to a preset period to obtain an image of the target person.
Specifically, video input is performed on a scene where the target person is located, and the video may include an action process of the target person. After the recorded video is obtained, frames of the video can be extracted according to a preset period, for example, one frame is extracted every five seconds in the preset period, and the extracted image is used as an image of the target person. The method has the advantages that the target person image is automatically acquired, manual operation processes are reduced, query time is saved, and query efficiency of the target person is improved.
In this embodiment, optionally, determining the target person region in the target person image includes: determining a target person area of the target person pedestrian frame, which is maximum and not shielded and/or random, from the target person image; wherein the pedestrian frame is an image area including at least one pedestrian.
Specifically, target person areas of the target person in different target person images may be different, and the target person is blocked by other persons in the moving process. The target person may be framed in a pedestrian frame, which may be a square frame including at least one pedestrian, for example, the pedestrian frame may only frame the target person. Fig. 2 is a schematic diagram of a pedestrian frame, and a dotted frame in fig. 2 is a pedestrian frame. When the target person moves, the distance between the target person and the camera changes, and the size of the pedestrian frame changes accordingly. And selecting at least one target person image, and acquiring a target person area from the target person image. For example, the target person image at three moments can be selected, which are the maximum moment of the target person pedestrian frame, the moment when the target person pedestrian frame is not blocked at all, and the random moment. When the target person area is determined, a target person pedestrian frame with the largest target person pedestrian frame and without the shielding of the target person pedestrian frame and/or a random target person image can be selected, wherein the largest target person pedestrian frame time is the time when the target person is closest to the camera. The effective result of setting like this lies in, obtains different target personnel regions to same target personnel, can follow the multi-angle and know the target personnel comprehensively, avoids omitting target personnel's image information, improves target personnel's inquiry precision.
In this embodiment, optionally, after acquiring the image of the target person, the method further includes: obtaining a target person detection network based on the faster RCNN network and the FPN network structure so as to detect and track the target person conveniently; the fasterRCNN network is used for detecting target personnel, and the FPN network is used for detecting the target personnel of the small target.
Specifically, a plurality of target person images can be directly acquired, and the target person area can be determined from the target person images. Or after the target person image is acquired, the target person in the image is detected and tracked, the behavior track of the target person is determined, and then a plurality of target person images are extracted from the image of the behavior track of the target person to determine the target person area. The target person detection Network can be obtained by adopting a fast RCNN (fast Convolutional Neural Network) Network and an FPN (Feature Pyramid Network) Network structure, and the target person is detected and tracked. The faster RCNN network and the FPN network can be used for detecting target personnel, wherein the FPN network greatly improves the performance of small target detection. For example, a strategy of collecting 5 frames per second may be adopted, images are extracted from the video, and a pre-trained network structure is used to detect the target person from the images. Tracking the target personnel by adopting an SORT (tracking) algorithm, recording the behavior tracks of the target personnel, and extracting a target personnel area of the pedestrian frame of the target personnel, which has the maximum pedestrian frame of the target personnel and is not shielded and/or random by the pedestrian frame of the target personnel, from the behavior tracks of the target personnel. The method has the advantages that the detection time is shortened by adopting a target tracking method, so that the real-time requirement is met, the target personnel images meeting the pedestrian frame requirement are prevented from being searched from a large number of target personnel images, the labor and the time are saved, and the query efficiency of the target personnel is improved.
S120, determining values of pixel points of the target personnel area in the RGB space, and clustering the target personnel area according to the values in the RGB space to obtain at least two value distribution families.
After the target person area is obtained, the value of the pixel point in the target person area in the color space is determined, and an RGB (Red Green Blue ) color space can be adopted. After the pixel values are obtained, clustering is performed on the pixel points of the same color by using a clustering algorithm to obtain at least two value distribution groups, for example, target person areas on target person images at three moments are extracted, an EM (Expectation value and maximum value) clustering algorithm can be used to cluster the pixels of the target person areas at the three moments, and a plurality of value distribution groups can be generated in the target person area at each moment. The color composition of the target person can be preliminarily obtained through the RGB color space, so that the colors in a value distribution family are kept consistent.
And S130, determining the value ratio of the target personnel area according to at least two value distribution families.
After the value distribution family is obtained, the value distribution family of the similar colors may exist, the value distribution family can be further adjusted, the value distribution family of the similar colors is combined into a distribution family, the type of the adjusted distribution family is determined, the proportion of the colors of the distribution family to the overall color of the target person is determined, and the color value ratio of the target person is determined.
In this embodiment, optionally, before determining the value ratio of the target person region according to at least two value distribution families, the method further includes: performing color difference analysis on at least two value distribution families based on an LAB space to obtain an adjustment result of the value distribution families; correspondingly, the step of determining the value ratio of the target personnel area according to at least two value distribution families comprises the following steps: and determining the value ratio of the target personnel area according to the adjustment result of the value distribution group.
Specifically, the obtained value distribution group is obtained based on an RGB color space, then an LAB (brightness, magenta to green range, yellow to blue range) color space is adopted to obtain values of pixels of a plurality of value distribution groups in the LAB color space, a mean value of each value distribution group is calculated, pairwise comparison is performed on different value distribution groups, and color difference analysis is performed. If the difference after comparison is smaller than a preset threshold value, the two value distribution families are considered to be the same color or similar colors, and the two value distribution families are combined; if the difference is larger than or equal to the preset threshold, no operation is performed, and the two value distribution families are considered as two families with irrelevant colors. And obtaining a new distribution family after adjustment, and determining the color composition of the target person and the value ratio of each color according to the new distribution family. For example, after the pixel values of each distribution group in L, A and B domains are obtained, the proportion of the pixel number of each distribution group in L, A and B domains is counted, and the value ratio of each color is obtained. The beneficial effect who sets up like this lies in, through adopting LAB color space, can extract target personnel's multiple colour composition, can not only handle the clothes of single colour, and through confirming target personnel's colour value and accounting for the ratio, effectively improve target personnel's inquiry precision.
And S140, storing the value ratio into a database so as to conveniently inquire the target personnel according to the data in the database.
The image to be inquired is a pre-uploaded photo which is consistent with the current wearing of the target person. When the target person goes to the field of camera shooting, the relevant information of the target person is uploaded in advance, and for example, the relevant information may include information such as a photo, a name, and an identification card currently worn by the target person. And after the value occupation ratio of the target person is obtained, storing the data of the value occupation ratio of the target person to a position corresponding to the target person in a database, and perfecting the data of the target person. When the target person needs to be inquired, the color data of the target person can be input, and the person information consistent with the color data can be searched from the database. For example, people wearing yellow, red, 50% green and 50% blue and 30% green and 70% blue are stored in the database, and the target people wearing green and blue are required to be searched, then green and blue are input, people wearing 50% green and 50% blue and 30% green and 70% blue are obtained, and the target people are searched from people wearing 50% green and 50% blue and people wearing 30% green and 70% blue, so that the problem that the target people can only be searched by inputting a single color in the prior art is solved, and the query efficiency of the target people is improved.
According to the technical scheme, the target person region is obtained from the target person image, the target person region is clustered to obtain a plurality of value distribution groups, the color value ratio of the target person is determined according to the pixel values of the value distribution groups in the color space, and the target person is inquired according to the value ratio. The problem of among the prior art carry out personnel's data storage and inquiry through the face or single color, the target person inquiry precision is low that causes is solved, through confirming the color composition, has improved the efficiency and the precision of target person inquiry.
Example two
Fig. 3 is a flowchart illustrating a target person query method according to a second embodiment of the present invention, which is further optimized based on the second embodiment. As shown in fig. 3, the method specifically includes the following steps:
s310, acquiring the target person image, and determining the target person area in the target person image.
S320, determining values of pixel points of the target personnel area in the RGB space, and clustering the target personnel area according to the values in the RGB space to obtain at least two value distribution families.
And S330, performing color difference analysis on the at least two value distribution families based on the LAB space to obtain an adjustment result of the value distribution families.
The method comprises the steps of determining the value of a pixel of each value distribution group in an LAB color space, calculating the pixel mean value of each value distribution group, carrying out color difference analysis according to the pixel mean value, and adjusting the value distribution group according to the color difference analysis result. And if the target personnel images at the three moments are extracted, performing chromatic aberration analysis on the target personnel at the three moments, and improving the adjustment precision of the value distribution group by combining the chromatic aberration analysis results at the three moments.
In this embodiment, optionally, performing color difference analysis on at least two value distribution families based on the LAB space to obtain an adjustment result of the value distribution family, including: determining the color difference value of at least two value distribution families according to the pixel mean value of the at least two value distribution families in the LAB space and the picture blurring degree; and adjusting the two value distribution families with the color difference value smaller than the preset threshold value into the distribution family of the same color.
Specifically, values of at least two value distribution family pixel points in an LAB color space are determined, a pixel mean value of the value distribution family is calculated, color difference analysis is carried out according to the pixel mean value and the image blurring degree, and a color difference value between the value distribution families is determined. If the color difference value is greater than or equal to a preset threshold value, the two value distribution families are considered to be two families with irrelevant colors; if the color difference value is smaller than the preset threshold value, the two value distribution families are combined into one distribution family, and the number of the distribution families is smaller than or equal to the number of the value distribution families. The method has the advantages that the pictures with different blurring degrees have different influences on the pixel value, the pictures with different qualities have different weight influence factors to influence the calculation of the color difference value by considering the blurring degrees of the pictures, the accuracy of color difference analysis is improved, and the query accuracy of target personnel is further improved.
In this embodiment, optionally, determining a color difference value of the at least two value distribution groups according to a pixel mean value of the at least two value distribution groups in the LAB space and a picture blur degree, includes: determining pixel difference values of any two value distribution families on L, A and B domains of an LAB space according to the pixel mean values of at least two value distribution families; determining the color difference of any two value distribution groups according to the pixel difference and the image fuzzy degree; the color difference value is calculated by the following formula:
y is a color difference between two value distribution groups, Δ L is a pixel difference between the two value distribution groups in the L domain, Δ a is a pixel difference between the two value distribution groups in the a domain, Δ B is a pixel difference between the two value distribution groups in the B domain, L, a, and B are preset parameters, and α is a picture blurring degree.
Specifically, after the pixel mean values of at least two value distribution groups at a certain moment are determined, the difference values of the pixel values of the two value distribution groups in L, A and B domains are respectively determined. And calculating the color difference values of the two value distribution groups according to the pixel difference values of the two value distribution groups in the three domains and the image blurring degree.
The calculation formula of the color difference value can be expressed as follows:
y is a color difference between two value distribution groups, Δ L is a pixel difference between the two value distribution groups in the L domain, Δ a is a pixel difference between the two value distribution groups in the a domain, Δ B is a pixel difference between the two value distribution groups in the B domain, L, a, and B are preset parameters, and α is a picture blurring degree. For example, l may be set to 1, a may be set to 1+0.045 Δ a, and b may be set to 1+0.015 Δ b. The beneficial effect who sets up like this lies in, can be according to different colours automatic setting and predetermine the parameter, make predetermine the parameter and have different values to different colours, improve target personnel's inquiry precision.
And S340, determining the value occupation ratio of the target personnel area according to the adjustment result of the value distribution group.
And S350, storing the value ratio into a database so as to conveniently inquire the target personnel according to the data in the database.
The method and the device for searching the target personnel in the color distribution of the LAB have the advantages that the target personnel area is obtained from the target personnel image, the target personnel area is clustered to obtain a plurality of value distribution families, the color difference value between different value distribution families is calculated according to the pixel value of the value distribution families in the LAB color space, the value distribution families of the same color are combined to obtain the distribution family of the color distribution of the target personnel, the color value ratio of the target personnel is determined, and the target personnel are inquired according to the value ratio. The problem of among the prior art carry out personnel's inquiry through the face or single color, the target personnel inquiry precision that causes is low is solved, through confirming the color composition, improved target personnel's inquiry efficiency and precision.
EXAMPLE III
Fig. 4 is a block diagram of a target person query device according to a third embodiment of the present invention, which is capable of executing a target person query method according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 4, the apparatus specifically includes:
a target area determination module 401, configured to obtain a target person image and determine a target person area in the target person image;
a value distribution family obtaining module 402, configured to determine values of pixel points of the target person region in an RGB space, and perform clustering processing on the target person region according to the values in the RGB space to obtain at least two value distribution families;
an occupation ratio determining module 403, configured to determine a value occupation ratio of the target person area according to the at least two value distribution families;
and the target query module 404 is configured to store the value ratio in a database, so as to query the target person according to the data in the database.
Optionally, the apparatus further comprises:
the color difference analysis module is used for performing color difference analysis on the at least two value distribution families based on the LAB space to obtain an adjustment result of the value distribution families;
correspondingly, the proportion determining module 403 is specifically configured to:
and determining the value ratio of the target personnel area according to the adjustment result of the value distribution group.
Optionally, the target area determining module 401 includes:
and the image obtaining unit is used for performing frame extraction on the video data input into the target person according to a preset period to obtain the image of the target person.
Optionally, the target area determining module 401 further includes:
the pedestrian frame determining unit is used for determining a target person area of the target person pedestrian frame, which is maximum in the target person pedestrian frame and not shielded and/or random in the target person pedestrian frame, from the target person image; wherein the pedestrian frame is an image area including at least one pedestrian.
Optionally, the color difference analysis module includes:
the color difference value determining unit is used for determining the color difference values of the at least two value distribution families according to the pixel mean values of the at least two value distribution families in the LAB space and the picture blurring degree;
and the distribution family adjusting unit is used for adjusting the two value distribution families with the color difference value smaller than the preset threshold value into the distribution families with the same color.
Optionally, the color difference determining unit is specifically configured to:
determining pixel difference values of any two value distribution families on L, A and B domains of an LAB space according to the pixel mean values of at least two value distribution families;
determining the color difference of any two value distribution groups according to the pixel difference and the image fuzzy degree;
the color difference value is calculated by the following formula:
y is a color difference between two value distribution groups, Δ L is a pixel difference between the two value distribution groups in the L domain, Δ a is a pixel difference between the two value distribution groups in the a domain, Δ B is a pixel difference between the two value distribution groups in the B domain, L, a, and B are preset parameters, and α is a picture blurring degree.
Optionally, the apparatus further comprises:
the target detection module is used for obtaining a target person detection network based on the master RCNN network and the FPN network structure so as to detect and track target persons conveniently; the fast RCNN network is used for detecting target personnel, and the FPN network is used for detecting the target personnel of the small target.
The embodiment of the invention obtains the target personnel area from the target personnel image, clusters the target personnel area to obtain a plurality of value distribution groups, determines the color value ratio of the target personnel according to the pixel values of the value distribution groups in the color space, and queries the target personnel according to the value ratio. The problem of among the prior art carry out personnel's inquiry through the face or single color, the target personnel inquiry precision that causes is low is solved, through confirming the color composition, improved target personnel's inquiry efficiency and precision.
Example four
Fig. 5 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. FIG. 5 illustrates a block diagram of an exemplary computer device 500 suitable for use in implementing embodiments of the invention. The computer device 500 shown in fig. 5 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present invention.
As shown in fig. 5, computer device 500 is in the form of a general purpose computing device. The components of computer device 500 may include, but are not limited to: one or more processors or processing units 501, a system memory 502, and a bus 503 that couples the various system components (including the system memory 502 and the processing unit 501).
The system memory 502 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)504 and/or cache memory 505. The computer device 500 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 506 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 503 by one or more data media interfaces. Memory 502 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 508 having a set (at least one) of program modules 507 may be stored, for instance, in memory 502, such program modules 507 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 507 generally perform the functions and/or methodologies of embodiments of the invention as described herein.
The computer device 500 may also communicate with one or more external devices 509 (e.g., keyboard, pointing device, display 510, etc.), with one or more devices that enable a user to interact with the computer device 500, and/or with any devices (e.g., network card, modem, etc.) that enable the computer device 500 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 511. Moreover, computer device 500 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network such as the Internet) via network adapter 512. As shown, network adapter 512 communicates with the other modules of computer device 500 over bus 503. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 501 executes various functional applications and data processing by running a program stored in the system memory 502, for example, implementing a method for querying a target person provided by an embodiment of the present invention, including:
acquiring a target person image, and determining a target person area in the target person image;
determining values of pixel points of a target personnel area in an RGB space, and clustering the target personnel area according to the values in the RGB space to obtain at least two value distribution families;
determining the value ratio of the target personnel area according to at least two value distribution families;
and storing the value ratio into a database so as to conveniently inquire the target personnel according to the data in the database.
EXAMPLE five
The fifth embodiment of the present invention further provides a storage medium containing computer-executable instructions, where a computer program is stored on the storage medium, and when the computer program is executed by a processor, the method for querying a target person according to the fifth embodiment of the present invention is implemented, where the method includes:
acquiring a target person image, and determining a target person area in the target person image;
determining values of pixel points of a target personnel area in an RGB space, and clustering the target personnel area according to the values in the RGB space to obtain at least two value distribution families;
determining the value ratio of the target personnel area according to at least two value distribution families;
and storing the value ratio into a database so as to conveniently inquire the target personnel according to the data in the database.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for querying a target person, comprising:
acquiring a target person image, and determining a target person area in the target person image;
determining values of pixel points of the target personnel area in an RGB space, and clustering the target personnel area according to the values in the RGB space to obtain at least two value distribution families;
determining the value ratio of the target personnel area according to the at least two value distribution families;
and storing the value ratio into a database so as to conveniently inquire the target personnel according to the data in the database.
2. The method of claim 1, wherein prior to determining the value fraction of the target person region from the at least two value distribution families, the method further comprises:
performing color difference analysis on the at least two value distribution families based on an LAB space to obtain an adjustment result of the value distribution families;
correspondingly, determining the value ratio of the target personnel area according to the at least two value distribution families comprises the following steps:
and determining the value occupation ratio of the target personnel area according to the adjustment result of the value distribution group.
3. The method of claim 1, wherein obtaining an image of a target person comprises:
and performing frame extraction on the video data input into the target person according to a preset period to obtain the image of the target person.
4. The method of claim 1, wherein determining a target person region in the target person image comprises:
determining the target person area of the target person pedestrian frame which is maximum and is not blocked by the target person pedestrian frame and/or random from the target person image; wherein the pedestrian frame is an image area including at least one pedestrian.
5. The method according to claim 2, wherein performing color difference analysis on the at least two value distribution families based on LAB space to obtain an adjustment result of the value distribution families comprises:
determining the color difference value of the at least two value distribution families according to the pixel mean value of the at least two value distribution families in the LAB space and the picture blurring degree;
and adjusting the two value distribution families with the color difference value smaller than the preset threshold value into the distribution family of the same color.
6. The method according to claim 5, wherein determining the color difference value of the at least two value distribution families according to the pixel mean value of the at least two value distribution families in the LAB space and the picture blur degree comprises:
determining pixel difference values of any two value distribution families on L, A and B domains of an LAB space according to the pixel mean values of the at least two value distribution families;
determining the color difference value of any two value distribution groups according to the pixel difference value and the picture fuzzy degree;
calculating the color difference value by the following formula:
y is a color difference between two value distribution groups, Δ L is a pixel difference between the two value distribution groups in the L domain, Δ a is a pixel difference between the two value distribution groups in the a domain, Δ B is a pixel difference between the two value distribution groups in the B domain, L, a, and B are preset parameters, and α is a picture blurring degree.
7. The method of claim 1, further comprising, after acquiring the image of the target person:
obtaining the target personnel detection network based on the fast RCNN network and the FPN network structure so as to be convenient for detecting and tracking the target personnel; the fast RCNN network is used for detecting the target personnel, and the FPN network is used for detecting the target personnel of the small target.
8. An apparatus for querying a target person, comprising:
the target area determining module is used for acquiring a target person image and determining a target person area in the target person image;
a value distribution family obtaining module, configured to determine values of pixel points of the target person region in an RGB space, and perform clustering processing on the target person region according to the values in the RGB space to obtain at least two value distribution families;
the proportion determining module is used for determining the value proportion of the target personnel area according to the at least two value distribution families;
and the target query module is used for storing the value ratio into a database so as to query the target personnel according to the data in the database.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the method of querying a target person as claimed in any one of claims 1 to 7.
10. A storage medium containing computer-executable instructions for performing the method of querying a target person of any one of claims 1-7 when executed by a computer processor.
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