CN111950421A - Face recognition system and trajectory tracking system - Google Patents
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Abstract
The application discloses face identification system and trajectory tracking system uses in face identification technique, and this face identification system includes: the system comprises a first data management module, a second data management module and a third data management module, wherein the first data management module is used for classifying a plurality of original images into a plurality of data containers according to values of at least two attributes of the original images, and the at least two attributes comprise a plurality of value ranges; the data container of the original image is identified according to the value ranges of the at least two attributes; and the first service module is used for acquiring the object to be recognized and the target values of the at least two attributes, matching a data container according to the target values of the at least two attributes, and performing face recognition in the matched data container according to the object to be recognized. According to the method and the device, the images are classified according to the attributes of the original images to form a small library, so that the comparison efficiency is improved in the face recognition process.
Description
Technical Field
The present application relates to face recognition technology, and in particular, to a face recognition system and a trajectory tracking system.
Background
In recent years, with the rapid development of face recognition technology, the implementation has been performed in various application scenarios, such as in the aspects of traffic system management, cell security management, public place people flow management, and payment authentication for commercial consumption, and meanwhile, as the range of application scenarios is continuously increased, the refinement, integration, and extension of application services are continuously performed, the capacity of a people base to be compared is gradually increased, meanwhile, the comparison data of a large number of people bases is more and more reduced, and the efficient need of comparison strategies is more brought up under the condition of low hardware investment, and further, the market and service needs are met in some embodiments.
As the capacity of the recognition library increases, the recognition efficiency decreases; a large number of wrong comparison results are easy to occur while a large number of comparisons are performed, and the accuracy and the authenticity of the service are influenced.
Disclosure of Invention
In view of this, the present application aims to: the face recognition system and the track tracking system are provided to improve the face recognition efficiency and the recognition accuracy under the same hardware condition.
In a first aspect, an embodiment of the present application provides:
a face recognition system comprising:
the system comprises a first data management module, a second data management module and a third data management module, wherein the first data management module is used for classifying a plurality of original images into a plurality of data containers according to values of at least two attributes of the original images, and the at least two attributes comprise a plurality of value ranges; the data container of the original image is identified according to the value ranges of the at least two attributes;
and the first service module is used for acquiring the object to be recognized and the target values of the at least two attributes, matching a data container according to the target values of the at least two attributes, and performing face recognition in the matched data container according to the object to be recognized.
In some embodiments, the data container is created by the first data management module upon obtaining a first raw image corresponding to the classification of the data container.
In some embodiments, the data containers are stored in the same number that is less than 10000.
In some embodiments, the first data management module is further to: when the storage quantity of the first data container reaches the maximum value, establishing a second data container with the same identification as the first data container;
and quantity balancing the first data container and the second data container so that the difference of the quantity of the original images in the first data container and the second data container or the ratio of the quantity of the original images in the first data container and the second data container is within a preset range.
In some embodiments, the range of values includes at least one value.
In some embodiments, the attributes include a time attribute and a location attribute.
In a second aspect, embodiments of the present application provide:
a trajectory tracking system comprising:
the second data management module is used for classifying the original images into a plurality of data containers according to the values of the time attribute and the position attribute of the original images, wherein the time attribute and the position attribute both comprise a plurality of value ranges; the data container of the original image is identified according to the value range of the time attribute and the value range of the position attribute;
and the second service module is used for acquiring an object to be recognized, a target value of the time attribute and a target value of the position attribute, matching a data container according to the target value of the time attribute and the target value of the position attribute, performing face recognition in the matched data container according to the object to be recognized to obtain a plurality of hit original images, and obtaining the track of the object to be recognized according to the time attribute and the position attribute of the plurality of hit original images.
In some embodiments, the data container is created by the second data management module upon obtaining a first raw image corresponding to the classification of the data container.
In some embodiments, the storage quantity of each of the second data containers is the same and is less than 10000.
In some embodiments, the data management module is further to: when the storage quantity of the first data container reaches the maximum value, establishing a second data container with the same identification as the first data container;
and quantity balancing the first data container and the second data container so that the difference of the quantity of the original images in the first data container and the second data container or the ratio of the quantity of the original images in the first data container and the second data container is within a preset range.
The method and the device classify the original images by setting at least two attributes to form a small library identified by the attributes, and when the images are identified, the data container is matched based on the target values of the attributes, so that the data volume of the original images of face identification can be greatly reduced, and simultaneously, the targets required by services can be accurately hit, so that the number of times of face identification is reduced, the matching time is shortened, and the images are matched in the small library, so that the probability of error matching can be reduced, and the accuracy is improved to a certain extent.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a block diagram of a face recognition system according to an embodiment of the present application;
FIG. 2 is a block diagram of modules of a trajectory tracking system provided in accordance with an embodiment of the present application;
fig. 3 is a schematic diagram of attribute partitioning according to an embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below through embodiments with reference to the accompanying drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all 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 application.
Referring to fig. 1, the present embodiment discloses a face recognition system, which includes:
the system comprises a first data management module, a second data management module and a third data management module, wherein the first data management module is used for classifying a plurality of original images into a plurality of data containers according to values of at least two attributes of the original images, and the at least two attributes comprise a plurality of value ranges; the data container of the original image is identified according to the value ranges of the at least two attributes;
and the first service module is used for acquiring the object to be recognized and the target values of the at least two attributes, matching a data container according to the target values of the at least two attributes, and performing face recognition in the matched data container according to the object to be recognized.
It is to be understood that in the present embodiment the attributes may be such as time, location, weather, photo content, etc. The time can be divided into value ranges according to hours, the positions can be divided according to a map, the position attribute is a coordinate, and the value of the position attribute can be limited by using a two-dimensional coordinate range. The weather can be divided into sunny days, cloudy days, rainy days and the like, and is marked by the equipment for acquiring the original image. The photo content may be classified according to actual conditions, for example, into an image of a lane and an image of a sidewalk. A range of values of an attribute may also be understood as an option, for example, for an attribute of image content, each range of values has in fact only one value. Then, by dividing the value ranges of the attributes, different classes can be divided under each attribute, and each class has a plurality of classes (i.e., a plurality of value ranges or options). Where N is the number of value ranges of the first attribute, and M is the number of value ranges of the second attribute.
For example, the time attribute may be divided into 24 hours and the location attribute may be divided into 48 locations, resulting in 1152 combinations. In this embodiment, 1152 data containers are generated during a day, and these data containers are associated with the original image. When the image at the position A in the 8 morning points needs to be matched, only the data container marked by the 8 morning points and the position A needs to be found, and the original image is found according to the mapping relation in the data container, so that the comparison range can be quickly reduced, the image identification efficiency is increased, meanwhile, the comparison is carried out in a small range, the occurrence of error results can be reduced, and the accuracy of the final result is integrally improved.
In some embodiments, the data container is created by the first data management module upon obtaining a first raw image corresponding to the classification of the data container. Wherein the data container is established only after the original images belonging to the category are obtained for more flexibility in space allocation.
In some embodiments, the data containers are stored in the same number that is less than 10000. In order to control the volume of the data container to avoid over-sizing the data container in this embodiment to slow down the matching process, the number of stores is therefore set within 10000. Of course, this value may be set to 5000 or 8000, etc., depending on the service needs and server performance.
In some embodiments, the first data management module is further to: when the storage quantity of the first data container reaches the maximum value, establishing a second data container with the same identification as the first data container;
and quantity balancing the first data container and the second data container so that the difference of the quantity of the original images in the first data container and the second data container or the ratio of the quantity of the original images in the first data container and the second data container is within a preset range.
It should be understood that, the smaller the storage quantity in the data container is, the faster the processing speed is, when the storage quantity of one data container is greater than the threshold, another data container with the same identifier as the data container can be generated, and the storage quantities of the two containers are equalized, generally, the ratio of the storage quantities of the two containers to the threshold is not greater than 10%, which is helpful for performing synchronous processing by using concurrent threads, and can increase the processing efficiency.
Referring to fig. 2, the present embodiment discloses a trajectory tracking system, which includes:
the second data management module is used for classifying the original images into a plurality of data containers according to the values of the time attribute and the position attribute of the original images, wherein the time attribute and the position attribute both comprise a plurality of value ranges; the data container of the original image is identified according to the value range of the time attribute and the value range of the position attribute;
and the second service module is used for acquiring an object to be recognized, a target value of the time attribute and a target value of the position attribute, matching a data container according to the target value of the time attribute and the target value of the position attribute, performing face recognition in the matched data container according to the object to be recognized to obtain a plurality of hit original images, and obtaining the track of the object to be recognized according to the time attribute and the position attribute of the plurality of hit original images.
It can be understood that, through the above-mentioned manner, the original images related to the object to be recognized may be screened out within a certain time and area, and the trajectory of the object to be recognized at the joint, for example, three original images where the object is found, is determined according to the time attribute and the position attribute of the original images, that is, 8 points 10 at the position a1, 8 points 15 at the position a2, and 8 points 20 at the position A3, and according to the chronological order, the trajectory of the object may be determined from a1 to a2 to A3. The scheme of the embodiment can increase the response speed of the service and increase the accuracy rate.
In some embodiments, the data container is created by the second data management module upon obtaining a first raw image corresponding to the classification of the data container.
In some embodiments, the storage quantity of each of the second data containers is the same and is less than 10000.
In some embodiments, the data management module is further to: when the storage quantity of the first data container reaches the maximum value, establishing a second data container with the same identification as the first data container;
and quantity balancing the first data container and the second data container so that the difference of the quantity of the original images in the first data container and the second data container or the ratio of the quantity of the original images in the first data container and the second data container is within a preset range.
It is to be understood that in a trajectory tracking system, technical features corresponding to a face recognition system may produce corresponding technical effects.
Referring to fig. 3, the present embodiment provides a division manner of the attribute of the original image, which is divided into three stages. The first stage is time and position, and the second stage and the third stage further divide the time and the position. This forms a tree-like structure to facilitate a quick determination of the identity of the data container to be searched.
In this embodiment, the A attribute is time and the B attribute is location, wherein the A attribute is divided into two large intervals of 0-12 and 12-24 in the second level and then subdivided to every hour under the two intervals. For the location attribute, for example, the derivative of Guangzhou city, the B attribute is divided into a cross show area, a river area, a wine area, etc. at the second level, and then divided into sub-areas managed by the derivative of each area at the third level. Assuming that the number of dispatches is 48 in this embodiment, a total of 1152 data containers are created in one day.
When searching for a data container, the data container may be searched based on the tree structure in fig. 3, for example, an original image in a district where the bouilli masu is located at 8 points in the morning is to be searched, firstly, a branch with time located at 0-12 points is determined, then, a third-level interval, namely 8-9 points, belonging to the branch is determined, meanwhile, a position attribute identifier of the data container is also determined to be located in the masu district according to a position, and then, a bouilli masu mas. Then, in combination with the two identifications, the data container can be determined. And then carrying out face recognition on the associated original image and the object to be recognized in the data container.
According to the definition defined by the data rule, the system automatically completes the establishment of the frame, forms the data container of the corresponding small library, completes the establishment of the mode frame container, reads the face and the data attribute thereof to be registered through multithreading and multiple concurrency, classifies the data rule definition through the data attribute, registers the corresponding face data to the corresponding data container, if the actual library content value of a single container is redundant, the container plans the library content value, the mechanism can newly add a second, a third or even more similar containers, and completes the starting work of the mode frame after completing the data registration action of the frame. After the data are classified according to the three levels, the collected data are subjected to partition processing according to the time and the position of the original data; for example, based on the example given above, if the peak value of the data volume stored in each container is 5000 photos, then by means of two data of time and location, the system automatically establishes data comparison library data containers according to each hour in combination with each association given by the given party, establishes 1152 data containers by default, and fills 1152 containers with historical acquisition data respectively, for example, one photo taken by three in the district of boulder of the area of deed 8:32 in the morning of yesterday, and automatically stores the photo in the container marked with 8 points and the given party of boulder; when one container is filled with 5000 photos, the system automatically generates second container continuous filling data according to the same data rule condition; assuming that the system generates by default that 8 dots and the container of the large stone have already been filled with 5000 photos of data as in the above condition, the system automatically establishes the container of 8 dots and the large stone to continue filling the subsequent same rule data, if the data is more, establishes more containers of 3, 4 and the like according to the rule, and so on.
According to the business needing to be compared or the photo data attribute, the system automatically matches one or more target libraries from the first level to the final face library attribute rule, after the matching of the target libraries is completed, the system starts to compare the faces in the one or more target libraries, returns the results with corresponding quantity according to the business needs, summarizes and sorts one or more sets of returned results, returns corresponding quantity according to the business needs, screens the summary list and returns the final results, and completes the whole set of comparison mode operation flow. For example, based on the foregoing example, after data loading is completed, the service needs to obtain the trajectory of the user in the area of the district of the wine at 8:00-10:00 through one certificate photo of three certificate photos, the system compares and collides the three certificate photos with the container of the large stone at 8 o 'clock and the container of the large stone at 9 o' clock, combines the results of the comparison of the two containers to the photos of the same person, sorts the photos according to scores from high to low, and returns the results to the service system for presentation or processing. Therefore, the quantity of the photos compared at a time is greatly reduced, the time and hardware resources of the comparison at a time are less, and the comparison accuracy is higher.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application 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 application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims (10)
1. A face recognition system, comprising:
the system comprises a first data management module, a second data management module and a third data management module, wherein the first data management module is used for classifying a plurality of original images into a plurality of data containers according to values of at least two attributes of the original images, and the at least two attributes comprise a plurality of value ranges; the data container of the original image is identified according to the value ranges of the at least two attributes;
and the first service module is used for acquiring the object to be recognized and the target values of the at least two attributes, matching a data container according to the target values of the at least two attributes, and performing face recognition in the matched data container according to the object to be recognized.
2. The face recognition system of claim 1, wherein: the data container is created by the first data management module upon obtaining a first raw image corresponding to the classification of the data container.
3. The face recognition system of claim 1, wherein the data containers are stored in the same number that is less than 10000.
4. The face recognition system of claim 3, wherein the first data management module is further configured to: when the storage quantity of the first data container reaches the maximum value, establishing a second data container with the same identification as the first data container;
and quantity balancing the first data container and the second data container so that the difference of the quantity of the original images in the first data container and the second data container or the ratio of the quantity of the original images in the first data container and the second data container is within a preset range.
5. The face recognition system of claim 1, wherein the range of values includes at least one value.
6. The face recognition system of claim 1, wherein the attributes include a time attribute and a location attribute.
7. A trajectory tracking system, comprising:
the second data management module is used for classifying the original images into a plurality of data containers according to the values of the time attribute and the position attribute of the original images, wherein the time attribute and the position attribute both comprise a plurality of value ranges; the data container of the original image is identified according to the value range of the time attribute and the value range of the position attribute;
and the second service module is used for acquiring an object to be recognized, a target value of the time attribute and a target value of the position attribute, matching a data container according to the target value of the time attribute and the target value of the position attribute, performing face recognition in the matched data container according to the object to be recognized to obtain a plurality of hit original images, and obtaining the track of the object to be recognized according to the time attribute and the position attribute of the plurality of hit original images.
8. The trajectory tracking system of claim 8, wherein the data container is created by the second data management module upon obtaining a first raw image corresponding to the classification of the data container.
9. The trajectory tracking system of claim 8, wherein each of the data containers is stored in the same quantity that is less than 10000.
10. The trajectory tracking system of claim 9, wherein the second data management module is further configured to: when the storage quantity of the first data container reaches the maximum value, establishing a second data container with the same identification as the first data container;
and quantity balancing the first data container and the second data container so that the difference of the quantity of the original images in the first data container and the second data container or the ratio of the quantity of the original images in the first data container and the second data container is within a preset range.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103246653A (en) * | 2012-02-03 | 2013-08-14 | 腾讯科技（深圳）有限公司 | Data processing method and device |
| CN106649784A (en) * | 2016-12-28 | 2017-05-10 | 深圳天珑无线科技有限公司 | Picture storing method, picture searching method, picture searching device and terminal |
| WO2017219679A1 (en) * | 2016-06-20 | 2017-12-28 | 杭州海康威视数字技术股份有限公司 | Method and device for establishing correspondence between rfid tags and persons, and method and device for trajectory tracking |
| CN107832680A (en) * | 2017-04-06 | 2018-03-23 | 小蚁科技（香港）有限公司 | Method, system and storage medium for the computerization of video analysis |
| CN108051777A (en) * | 2017-12-01 | 2018-05-18 | 北京迈格威科技有限公司 | Method for tracing, device and the electronic equipment of target |
| CN110866469A (en) * | 2019-10-30 | 2020-03-06 | 腾讯科技（深圳）有限公司 | Human face facial features recognition method, device, equipment and medium |
-
2020
- 2020-08-05 CN CN202010775916.XA patent/CN111950421A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103246653A (en) * | 2012-02-03 | 2013-08-14 | 腾讯科技（深圳）有限公司 | Data processing method and device |
| WO2017219679A1 (en) * | 2016-06-20 | 2017-12-28 | 杭州海康威视数字技术股份有限公司 | Method and device for establishing correspondence between rfid tags and persons, and method and device for trajectory tracking |
| CN106649784A (en) * | 2016-12-28 | 2017-05-10 | 深圳天珑无线科技有限公司 | Picture storing method, picture searching method, picture searching device and terminal |
| CN107832680A (en) * | 2017-04-06 | 2018-03-23 | 小蚁科技（香港）有限公司 | Method, system and storage medium for the computerization of video analysis |
| CN108051777A (en) * | 2017-12-01 | 2018-05-18 | 北京迈格威科技有限公司 | Method for tracing, device and the electronic equipment of target |
| CN110866469A (en) * | 2019-10-30 | 2020-03-06 | 腾讯科技（深圳）有限公司 | Human face facial features recognition method, device, equipment and medium |
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