CN111429476A - Method and device for determining action track of target person - Google Patents
Method and device for determining action track of target person Download PDFInfo
- Publication number
- CN111429476A CN111429476A CN201910020875.0A CN201910020875A CN111429476A CN 111429476 A CN111429476 A CN 111429476A CN 201910020875 A CN201910020875 A CN 201910020875A CN 111429476 A CN111429476 A CN 111429476A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- target person
- human body
- body structural
- point set
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30196—Human being; Person
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30196—Human being; Person
- G06T2207/30201—Face
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a method and a device for determining an action track of a target person, and belongs to the field of intelligent monitoring. The method comprises the following steps: determining a first monitoring point set which is captured to the target person based on the reference human face image data of the target person and the monitoring images shot by the monitoring points; acquiring human body structural data of the target person, wherein the human body structural data of the target person is acquired according to monitoring images shot by monitoring points in the first monitoring point set; determining a second monitoring point set of the target person based on the human body structural data of the target person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set; and determining the action track of the target person based on the information of the first monitoring point set and the information of the second monitoring point set. By adopting the method and the device, the accuracy of determining the action track of the target person can be improved.
Description
Technical Field
The invention relates to the field of intelligent monitoring, in particular to a method and a device for determining a target person action track.
Background
With the development of image recognition technology, the server can recognize the target person from the monitored video according to the reference image of the target person.
The server can acquire a plurality of character images from a monitoring video shot by a monitoring point, and then can respectively compare the reference image of the target character with the plurality of character images to search the target character. When the target person is found, the server can determine the position information of the corresponding monitoring point and the shooting time information as track point data, and then determine the action track of the target person according to all track point data so as to assist case processing of the public security department.
In the process of implementing the invention, the inventor finds that the prior art has at least the following problems:
when determining the action track of the target person, the server generally recognizes a face image of the person. In reality, many surveillance videos may not have face images captured, and the obtained trajectory point data is less, so that the accuracy of the obtained action trajectory of the target person is low.
Disclosure of Invention
In order to solve the problems in the prior art, embodiments of the present invention provide a method and an apparatus for determining an action trajectory of a target person. The technical scheme is as follows:
in a first aspect, a method for determining an action track of a target person is provided, and the method includes:
determining a first monitoring point set which is captured to the target person based on the reference human face image data of the target person and the monitoring images shot by the monitoring points;
acquiring human body structural data of the target person, wherein the human body structural data of the target person is acquired according to monitoring images shot by monitoring points in the first monitoring point set;
determining a second monitoring point set of the target person based on the human body structural data of the target person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set;
and determining the action track of the target person based on the information of the first monitoring point set and the information of the second monitoring point set.
Optionally, the method further includes:
if the number of the monitoring points in the first monitoring point set is larger than a first preset threshold value, acquiring human body structural data of at least one peer person of the target person according to the monitoring image of the monitoring points in the first monitoring point set, wherein the first preset threshold value is larger than 1, and the peer person is a person meeting the action track similarity condition with the target person;
determining a second monitoring point set of the target person based on the human body structural data of the target person and the monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set, wherein the determining comprises the following steps:
and determining a second monitoring point set of the target person based on the human body structural data of the target person, the human body structural data of the at least one peer person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set.
Optionally, the obtaining human body structural data of at least one peer person of the target person according to the monitoring image of the monitoring point in the first monitoring point set includes:
acquiring face image data shot by a plurality of monitoring points in the first monitoring point set and snapshot time corresponding to each piece of face image data;
determining face image data meeting the condition that the capturing time is close to the target figure face image data based on the acquired face image data and the capturing time corresponding to each piece of face image data;
among the determined face image data, dividing the face image data with the similarity larger than a second preset threshold into an image group;
determining at least one image group containing face image data of which the number is larger than a third preset threshold, and determining human body structural data corresponding to the face image data in the at least one image group as human body structural data of at least one same-line person of the target person.
Optionally, the determining, based on the human body structural data of the target person, the human body structural data of the at least one peer person, and the monitoring images captured by other monitoring points associated with the monitoring points in the first monitoring point set, a second monitoring point set where the target person is captured includes:
acquiring human body structural data of other monitoring points related to the monitoring point in the first monitoring point set;
determining all monitoring points of the other associated monitoring points which simultaneously satisfy at least the following two conditions as a second monitoring point set:
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the target person is greater than a preset value;
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the people in the same line is greater than a preset value.
Optionally, the other monitoring points associated with the monitoring point in the first monitoring point set are monitoring points whose distance from any monitoring point in the first monitoring point set is smaller than a fourth preset threshold.
In a second aspect, an apparatus for determining an action track of a target person is provided, the apparatus comprising:
the determining module is used for determining a first monitoring point set which is captured to the target person based on the reference human face image data of the target person and the monitoring images shot by the monitoring points;
the acquisition module is used for acquiring the human body structural data of the target person, and the human body structural data of the target person is acquired according to the monitoring images shot by the monitoring points in the first monitoring point set;
the determining module is further configured to determine a second monitoring point set in which the target person is shot based on the human body structural data of the target person and monitoring images shot by other monitoring points associated with the monitoring points in the first monitoring point set; and determining the action track of the target person based on the information of the first monitoring point set and the information of the second monitoring point set.
Optionally, the obtaining module is further configured to:
if the number of the monitoring points in the first monitoring point set is larger than a first preset threshold value, acquiring human body structural data of at least one peer person of the target person according to the monitoring image of the monitoring points in the first monitoring point set, wherein the first preset threshold value is larger than 1, and the peer person is a person meeting the action track similarity condition with the target person;
the determining module is configured to:
and determining a second monitoring point set of the target person based on the human body structural data of the target person, the human body structural data of the at least one peer person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set.
Optionally, the obtaining module is configured to:
acquiring face image data shot by a plurality of monitoring points in the first monitoring point set and snapshot time corresponding to each piece of face image data;
determining face image data meeting the condition that the capturing time is close to the target figure face image data based on the acquired face image data and the capturing time corresponding to each piece of face image data;
among the determined face image data, dividing the face image data with the similarity larger than a second preset threshold into an image group;
determining at least one image group containing face image data of which the number is larger than a third preset threshold, and determining human body structural data corresponding to the face image data in the at least one image group as human body structural data of at least one same-line person of the target person.
Optionally, the determining module is configured to:
acquiring human body structural data of other monitoring points related to the monitoring point in the first monitoring point set;
determining all monitoring points of the other associated monitoring points which simultaneously satisfy at least the following two conditions as a second monitoring point set:
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the target person is greater than a preset value;
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the people in the same line is greater than a preset value.
Optionally, the other monitoring points associated with the monitoring point in the first monitoring point set are monitoring points whose distance from any monitoring point in the first monitoring point set is smaller than a fourth preset threshold.
In a third aspect, a system for determining an action track of a target person is provided, the system comprising a server and a plurality of monitoring points, wherein:
the monitoring point is used for shooting a monitoring image in a monitoring range and sending the shot monitoring image to the server;
the server is used for determining a first monitoring point set which is captured to the target person based on the reference human face imaging data of the target person and the monitoring images shot by the monitoring points; acquiring human body structural data of the target person, wherein the human body structural data of the target person is acquired according to monitoring images shot by monitoring points in the first monitoring point set; determining a second monitoring point set of the target person based on the human body structural data of the target person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set; and determining the action track of the target person based on the information of the first monitoring point set and the information of the second monitoring point set.
In a fourth aspect, a server is provided, which includes a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method for determining the action track of the target person according to the first aspect.
In a fifth aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to implement the method for determining the action track of the target person according to the first aspect.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, a server determines a first monitoring point set of a target person based on reference face image data of the target person and monitoring images shot by monitoring points, then obtains human body structural data of the target person from the corresponding monitoring images, determines a second monitoring point set of the target person based on the human body structural data of the target person and the monitoring images shot by other monitoring points, and determines the action track of the target person based on the first monitoring point set and the second monitoring point set. Therefore, the target person is searched based on the face image data, the human body structural data of the target person is obtained, the target person is searched based on the human body structural data, more monitoring points for shooting the target person can be searched, the action track of the target person is refined, and the accuracy of determining the action track of the target person is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, 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 flowchart of a method for determining an action track of a target person according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an implementation environment provided by an embodiment of the invention;
FIG. 3 is a schematic diagram of an action trajectory of a target person according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an apparatus for determining an action trajectory of a target person according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a server according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The embodiment of the invention provides a method for determining an action track of a target person, which can be realized by a server.
The server may include a processor, memory, transceiver, etc. The processor, which may be a CPU (central processing Unit) or the like, may be used to determine trajectory point data of a target person, obtain a face image, obtain a body image, determine an action trajectory of the target person, and the like. The memory may be a RAM (Random access memory), a Flash memory, and the like, and may be configured to store received data, data required by the processing process, data generated in the processing process, and the like, such as a face image, a human body image, trajectory point data, a motion trajectory, and the like. Transceivers, which may be used for data transmission with other devices, may include antennas, matching circuits, modems, etc.
As shown in fig. 1, the processing flow of the method may include the following steps:
in step 101, the server determines a first monitoring point set in which a target person is captured based on reference face image data of the target person and monitoring images captured at monitoring points.
The data in the first monitoring point set can include position information of the monitoring points and snapshot time for shooting the target person.
In an implementation, the monitoring point may be a monitoring camera for capturing monitoring images within a monitoring range. When the public security department processes the case, the monitoring images of all monitoring points may be searched to judge the action track of the target person.
The monitoring points may mainly include three types: face cameras, smart cameras and ordinary cameras. The main functions of the three monitoring points can be different, for example, the face camera can acquire the face image of a passing pedestrian through an embedded intelligent algorithm, the intelligent camera can acquire the body image of the passing pedestrian through the embedded intelligent algorithm, and the common camera can only shoot the monitoring image in the monitoring range. However, as long as the installation angle of the monitoring camera is proper and the definition of the shot monitoring image meets the requirement, the monitoring image can be processed by the server at the rear end to obtain the face image or the human body image in the monitoring image. The above intelligent algorithm for acquiring the face image or the body image may be a deep learning algorithm, and is not limited herein.
When the target person is searched in the monitored image, the searching is carried out based on the face image data, and compared with the searching based on the human body structural data, the searching accuracy is higher. Therefore, in order to find the target person in the monitored image, the user may first upload the reference face image data of the target person to the server. Furthermore, when the action track of the target person is determined, the server can acquire the monitored images shot by each monitoring point from the database, and search the facial image data of the target person in the monitored images according to the reference facial image data of the target person. When the face image data of the target person is found in the monitored image, indicating that the target person appears in the monitoring range, the server may add the position information of the monitored point where the target person is shot and the snapshot time to the first monitored point set. After the monitoring videos of the monitoring points are searched, the first monitoring point set may include only one monitoring point or may include a plurality of monitoring points.
Optionally, before the face image or the body image is obtained, the person image in the monitoring image may be obtained, and the corresponding processing may be as follows: and acquiring each person image in the monitoring images shot by each monitoring point, and determining the face image data in each person image.
In implementation, when the monitoring point or the server acquires the face image in the monitoring image, not only the face image but also the person image of each person may be acquired. The personal image may be an image including the entire human body. When the face image is obtained, the face part of the human image can be obtained; in acquiring the human body image, the human body portion of the human body image may be acquired.
In order to calculate the similarity between images conveniently, mathematical modeling may be performed on the images to obtain a string of binary codes, that is, an image model, such as a human face image model or a human body image model. Meanwhile, the attribute information of the image can also be acquired through an image recognition technology, for example, the attribute information of the face image can be gender, race and the like, the attribute information of the human body image can be clothes color, clothes style and the like, and the attribute information can be structured information. The face image data or the human body structural data can comprise the image model and the attribute information, the face camera can also acquire the face image model through an intelligent algorithm, and similarly, the intelligent camera can also acquire the human body image model through the intelligent algorithm. The face image data or the body structure data acquired by the face camera, the smart camera or the server may be stored in a database. The implementation environment is schematically shown in fig. 2.
The server can comprise a storage server and a processing server, wherein the storage server can be used for storing the database, the processing server can be used for executing the method flow for determining the action track of the target person, and the processing server can perform data interaction with the storage server. Of course, both storage and processing may be performed by one server, and the embodiment of the present invention is implemented by one server as an example.
Optionally, the specific process of the server determining the first monitoring point set may be as follows: acquiring each face image data in a monitoring image shot by each monitoring point; determining the similarity between the reference facial image data of the target person and each acquired facial image data, and determining the target facial image data with the similarity larger than a preset threshold; and adding the position information and the snapshot time of the monitoring point corresponding to the target face image data into the first monitoring point set.
In implementation, the server may obtain the face image data from the database, and then sequentially calculate the similarity between each face image data and the reference face image data of the target person according to a similarity calculation method. Then, the server may select target face image data with similarity greater than a preset threshold as the found face image data of the target person, determine the position information and the snapshot time of the corresponding monitoring point as at least one track point data, and add the track point data to the first monitoring point set so as to be used for generating the action track of the target person.
In step 102, the server obtains the human body structural data of the target person.
And acquiring the human body structural data of the target person according to the monitoring images shot by the monitoring points in the first monitoring point set.
In implementation, in the above process, the server searches the data after the face image of the target person is found in the monitored image, and can obtain the human body structural data of the target person in the corresponding monitored image. Because the clothing of the target person has a large influence on the human body structural data, under the condition that the current clothing of the target person cannot be known in advance, the target person can be searched based on the face image data, and then the current human body structural data of the target person is obtained.
Optionally, in the above process, before the face image or the body image is obtained, the person image in the monitored image may be obtained, and the server may obtain the target body structural data in the target person image to which the target face image data belongs, as the body structural data of the target person.
Of course, if the monitoring point can extract the human body structure data and upload the data to the server, the server may also obtain the human body structure data sent by the monitoring point. The specific way of extracting the human body structure data by the monitoring point is the same as the above way, and is not described herein again.
In step 103, the server determines a second monitoring point set in which the target person is shot based on the human body structural data of the target person and the monitoring images shot by other monitoring points associated with the monitoring points in the first monitoring point set.
The related monitoring points may refer to monitoring points that satisfy a preset relationship, for example, monitoring points that are preset to be in the same block or the same street. Or, the other monitoring points associated with the monitoring points in the first monitoring point set may also be monitoring points whose distance to any monitoring point in the first monitoring point set is less than a fourth preset threshold. In this embodiment, the specific association manner of the monitoring points is not limited.
In the implementation, the number of the human body structural data acquired from the monitored image may be more than that of the human face image data, and after the server determines the first monitoring point set according to the human face image data of the target person in the above process, other human body structural data of the target person may be searched in the monitored images shot by other monitoring points according to the human body structural data of the target person. The server may use the position information of the monitored point where the target person is photographed and the photographing time information as a supplementary trajectory point data whenever other human body structural data of the target person is found in the monitored image.
Optionally, the specific process of the server determining the second monitoring point set may be as follows: acquiring each human body structural data in monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set; determining the similarity between the human body structural data of the target person and each human body structural data, and determining the human body structural data with the similarity larger than a preset threshold value as other human body structural data of the target person; and adding the position information and the snapshot time of the monitoring points corresponding to other human body structural data into a second monitoring point set.
In an implementation, the server may obtain, from the database, the human body structural data in the monitoring images captured by the other monitoring points associated with the monitoring point in the first monitoring point set, which has been described above in a specific manner, and is not described here again. Similar to the similarity of the face image data, the server may calculate the similarity between the obtained human body structural data and the human body structural data of the target person obtained in step 102 according to a similarity calculation method. Then, the server can select the human body structural data with the similarity larger than the preset threshold value as other found human body structural data of the target person, determine the position information and the snapshot time of the corresponding monitoring point as at least one supplementary track point data, and add the supplementary track point data into the second monitoring point set so as to obtain a more accurate action track of the target person.
The other human structured data determined in the above process may belong to other persons similar to the target person clothing, so that a plurality of possible target person action trajectories can be generated. In some practical cases, the target person may have approximately the same trajectory as some of the fellow persons. Therefore, the human body structural data of the character in the same line can be introduced as assistance to determine the supplementary track point data of the action track so as to determine the more accurate action track of the target character, and the corresponding processing can be as follows: if the number of the monitoring points in the first monitoring point set is larger than a first preset threshold value, acquiring human body structural data of at least one peer person of the target person according to the monitoring image of the monitoring points in the first monitoring point set; and determining a second monitoring point set for shooting the target person based on the human body structural data of the target person, the human body structural data of at least one person in the same line and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set.
The first preset threshold is greater than 1, the people in the same row refer to people meeting the action track similarity condition with the target people, and meeting the action track similarity can refer to the number of the same track points being greater than the third preset threshold.
In implementation, if the number of the monitoring points in the first monitoring point set is greater than a first preset threshold, which should be at least greater than 1, people other than the target person and appearing in the monitoring points at the same time can be searched in the monitored images of the monitoring points, and the possibility that the people are the co-walking people of the target person is high. Further, the server may extract human body structural data of at least one peer person from the monitored image. Then, in the process of searching other human body structural data of the target person, whether other human body structural data of the same-row person exist in the monitored image or not is searched, if so, the position information and the snapshot time of the corresponding monitored point are determined as at least one supplementary track point data and added into a second monitored point set.
Optionally, the specific process of determining the human body structural data of the peer people may be as follows: acquiring face image data shot by a plurality of monitoring points in a first monitoring point set and snapshot time corresponding to each piece of face image data; determining face image data which meets the condition that the face image data of the target person is close to the snapshot time based on the acquired face image data and the snapshot time corresponding to each piece of face image data; among the determined face image data, dividing the face image data with the similarity larger than a second preset threshold into an image group; and determining at least one image group containing facial image data of which the number is larger than a third preset threshold, and determining human body structural data corresponding to the facial image data in the at least one image group as human body structural data of at least one peer person of the target person.
In implementation, for a situation that before the face image or the body image is obtained, the person image in the monitoring image may be obtained, the server may obtain, based on each snapshot time in the first monitoring point set, face image data of each person image that meets a condition that the snapshot time is close to (e.g., within 1 minute before and after) in the monitoring points of the first monitoring point set, and obtain a face image data set (hereinafter, referred to as a set for short) corresponding to each monitoring point. Then, the face image data in each set is respectively compared with the face image data in other sets to calculate the similarity. For one face image data, if the face image data with the similarity greater than the second similarity threshold is found in other sets, the face image data can be considered to belong to the same person, and can be divided into the same image group to represent one person. If the number of face image data in the image group is larger than a third preset threshold, the person may be regarded as a peer person of the target person. Furthermore, the server can obtain corresponding human body structural data from the person image of the person in the same line, namely obtain the human body structural data of the person in the same line. Of course, the number of the peer people determined by the server may be one or more.
Optionally, the specific process of determining the second monitoring point set by combining the human body structural data of the people in the same row may be as follows: acquiring human body structural data of other monitoring points related to the monitoring points in the first monitoring point set; and determining all the monitoring points which at least meet the following two conditions at the same time in the other associated monitoring points as a second monitoring point set. The two conditions are as follows:
(1) the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the target person is greater than a preset value;
(2) the human body structural data of the monitoring point has human body structural data, the similarity of the human body structural data of the people in the same row with the human body structural data of the people in the same row is larger than a preset value.
In implementation, after the server determines the face image data of the next person in the same line in the above process, the server may obtain the human body structural data in the image of the person to which the server belongs, and determine the human body structural data as the person in the same line. And one peer person may have multiple human structured data, and subsequently other human structured data for the peer person may be looked up based on the multiple human structured data.
The server can obtain human body structural data in a monitoring image shot by monitoring points in a certain range around the monitoring point determined as the track point from the database. The server may calculate the similarity of the obtained human body structured data and the human body structured data of the target person obtained in step 102 according to a similarity algorithm. Then, the server can select the human body structural data with the similarity larger than the preset value as other found human body structural data of the target person.
The server can also calculate the similarity of the human body structural data of the people in the same line in the obtained human body structural data, and can select the human body structural data with the similarity larger than a preset value to be used as other searched human body structural data of the people in the same line.
If other human body structural data of the target person and the peer person exist in the monitored image of one monitoring point, the server can determine the position information and the snapshot time of the corresponding monitoring point as at least one supplementary track point data, and add the supplementary track point data to the second monitoring point set so as to obtain a more accurate action track of the target person. The above-mentioned case where the other human body structural data of the target person and the peer person exist simultaneously may be the case where the target person and all the peer persons exist simultaneously, or the case where the target person and at least one of the peer persons exist simultaneously, which is not limited herein.
It should be noted that all the threshold values involved in the above process can be set by the skilled person according to the actual needs, and are not limited herein.
In step 104, the server determines the action track of the target person based on the information of the first monitoring point set and the information of the second monitoring point set.
In implementation, the server may use each monitoring point and the snapshot time as track point information according to the position information and the snapshot time of the monitoring points in the first monitoring point set and the second monitoring point set, arrange each monitoring point, and generate the action track of the target person. And the server can feed back the data of each track point in the action track to the user, or display the action track of the target person on the map after rendering so that the user can analyze and judge. The action track diagram of the target person is shown in fig. 3.
In the embodiment of the invention, a server determines a first monitoring point set of a target person based on reference face image data of the target person and monitoring images shot by monitoring points, then obtains human body structural data of the target person from the corresponding monitoring images, determines a second monitoring point set of the target person based on the human body structural data of the target person and the monitoring images shot by other monitoring points, and determines the action track of the target person based on the first monitoring point set and the second monitoring point set. Therefore, the target person is searched based on the face image data, the human body structural data of the target person is obtained, the target person is searched based on the human body structural data, more monitoring points for shooting the target person can be searched, the action track of the target person is refined, and the accuracy of determining the action track of the target person is improved.
Based on the same technical concept, the embodiment of the invention also provides a device for determining the action track of the target person, and the device can be the server in the above method embodiment. As shown in fig. 4, the apparatus includes:
a determining module 410, configured to determine a first monitoring point set snapped to a target person based on reference face image data of the target person and monitoring images captured at monitoring points;
an obtaining module 420, configured to obtain human body structural data of the target person, where the human body structural data of the target person is obtained according to a monitoring image captured by a monitoring point in the first monitoring point set;
the determining module 410 is further configured to determine a second monitoring point set in which the target person is captured based on the human body structural data of the target person and monitoring images captured by other monitoring points associated with the monitoring points in the first monitoring point set; and determining the action track of the target person based on the information of the first monitoring point set and the information of the second monitoring point set.
Optionally, the obtaining module 420 is further configured to:
if the number of the monitoring points in the first monitoring point set is larger than a first preset threshold value, acquiring human body structural data of at least one peer person of the target person according to the monitoring image of the monitoring points in the first monitoring point set, wherein the first preset threshold value is larger than 1, and the peer person is a person meeting the action track similarity condition with the target person;
the determining module 410 is configured to:
and determining a second monitoring point set of the target person based on the human body structural data of the target person, the human body structural data of the at least one peer person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set.
Optionally, the obtaining module 420 is configured to:
acquiring face image data shot by a plurality of monitoring points in the first monitoring point set and snapshot time corresponding to each piece of face image data;
determining face image data meeting the condition that the capturing time is close to the target figure face image data based on the acquired face image data and the capturing time corresponding to each piece of face image data;
among the determined face image data, dividing the face image data with the similarity larger than a second preset threshold into an image group;
determining at least one image group containing face image data of which the number is larger than a third preset threshold, and determining human body structural data corresponding to the face image data in the at least one image group as human body structural data of at least one same-line person of the target person.
Optionally, the determining module 410 is configured to:
acquiring human body structural data of other monitoring points related to the monitoring point in the first monitoring point set;
determining all monitoring points of the other associated monitoring points which simultaneously satisfy at least the following two conditions as a second monitoring point set:
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the target person is greater than a preset value;
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the people in the same line is greater than a preset value.
Optionally, the other monitoring points associated with the monitoring point in the first monitoring point set are monitoring points whose distance from any monitoring point in the first monitoring point set is smaller than a fourth preset threshold.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
In the embodiment of the invention, a server determines a first monitoring point set of a target person based on reference face image data of the target person and monitoring images shot by monitoring points, then obtains human body structural data of the target person from the corresponding monitoring images, determines a second monitoring point set of the target person based on the human body structural data of the target person and the monitoring images shot by other monitoring points, and determines the action track of the target person based on the first monitoring point set and the second monitoring point set. Therefore, the target person is searched based on the face image data, the human body structural data of the target person is obtained, the target person is searched based on the human body structural data, more monitoring points for shooting the target person can be searched, the action track of the target person is refined, and the accuracy of determining the action track of the target person is improved.
It should be noted that: the apparatus for determining a target person action track according to the above embodiments is only illustrated by dividing the above function modules when determining the target person action track, and in practical applications, the function distribution may be performed by different function modules according to needs, that is, the internal structure of the server is divided into different function modules to perform all or part of the functions described above. In addition, the apparatus for determining the action trajectory of the target person and the method for determining the action trajectory of the target person provided by the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.
Based on the same technical concept, the embodiment of the present invention further provides a system for determining an action track of a target person, which may include a server and a plurality of monitoring points, wherein:
the monitoring point is used for shooting a monitoring image in a monitoring range and sending the shot monitoring image to the server;
the server is used for determining a first monitoring point set which is captured to the target person based on the reference human face imaging data of the target person and the monitoring images shot by the monitoring points; acquiring human body structural data of the target person, wherein the human body structural data of the target person is acquired according to monitoring images shot by monitoring points in the first monitoring point set; determining a second monitoring point set of the target person based on the human body structural data of the target person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set; and determining the action track of the target person based on the information of the first monitoring point set and the information of the second monitoring point set.
Fig. 5 is a schematic structural diagram of a server according to an embodiment of the present invention, where the server 500 may generate relatively large differences due to different configurations or performances, and may include one or more processors (CPUs) 501 and one or more memories 502, where the memory 502 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 501 to implement the following method steps for determining an action trajectory of a target person:
determining a first monitoring point set which is captured to the target person based on the reference human face image data of the target person and the monitoring images shot by the monitoring points;
acquiring human body structural data of the target person, wherein the human body structural data of the target person is acquired according to monitoring images shot by monitoring points in the first monitoring point set;
determining a second monitoring point set of the target person based on the human body structural data of the target person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set;
and determining the action track of the target person based on the information of the first monitoring point set and the information of the second monitoring point set.
Optionally, the at least one instruction is loaded and executed by the processor 501 to implement the following method steps:
if the number of the monitoring points in the first monitoring point set is larger than a first preset threshold value, acquiring human body structural data of at least one peer person of the target person according to the monitoring image of the monitoring points in the first monitoring point set, wherein the first preset threshold value is larger than 1, and the peer person is a person meeting the action track similarity condition with the target person;
and determining a second monitoring point set of the target person based on the human body structural data of the target person, the human body structural data of the at least one peer person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set.
Optionally, the at least one instruction is loaded and executed by the processor 501 to implement the following method steps:
acquiring face image data shot by a plurality of monitoring points in the first monitoring point set and snapshot time corresponding to each piece of face image data;
determining face image data meeting the condition that the capturing time is close to the target figure face image data based on the acquired face image data and the capturing time corresponding to each piece of face image data;
among the determined face image data, dividing the face image data with the similarity larger than a second preset threshold into an image group;
determining at least one image group containing face image data of which the number is larger than a third preset threshold, and determining human body structural data corresponding to the face image data in the at least one image group as human body structural data of at least one same-line person of the target person.
Optionally, the at least one instruction is loaded and executed by the processor 501 to implement the following method steps:
acquiring human body structural data of other monitoring points related to the monitoring point in the first monitoring point set;
determining all monitoring points of the other associated monitoring points which simultaneously satisfy at least the following two conditions as a second monitoring point set:
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the target person is greater than a preset value;
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the people in the same line is greater than a preset value.
Optionally, the other monitoring points associated with the monitoring point in the first monitoring point set are monitoring points whose distance from any monitoring point in the first monitoring point set is smaller than a fourth preset threshold.
In the embodiment of the invention, a server determines a first monitoring point set of a target person based on reference face image data of the target person and monitoring images shot by monitoring points, then obtains human body structural data of the target person from the corresponding monitoring images, determines a second monitoring point set of the target person based on the human body structural data of the target person and the monitoring images shot by other monitoring points, and determines the action track of the target person based on the first monitoring point set and the second monitoring point set. Therefore, the target person is searched based on the face image data, the human body structural data of the target person is obtained, the target person is searched based on the human body structural data, more monitoring points for shooting the target person can be searched, the action track of the target person is refined, and the accuracy of determining the action track of the target person is improved.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (13)
1. A method for determining an action track of a target person, the method comprising:
determining a first monitoring point set which is captured to the target person based on the reference human face image data of the target person and the monitoring images shot by the monitoring points;
acquiring human body structural data of the target person, wherein the human body structural data of the target person is acquired according to monitoring images shot by monitoring points in the first monitoring point set;
determining a second monitoring point set of the target person based on the human body structural data of the target person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set;
and determining the action track of the target person based on the information of the first monitoring point set and the information of the second monitoring point set.
2. The method of claim 1, further comprising:
if the number of the monitoring points in the first monitoring point set is larger than a first preset threshold value, acquiring human body structural data of at least one peer person of the target person according to the monitoring image of the monitoring points in the first monitoring point set, wherein the first preset threshold value is larger than 1, and the peer person is a person meeting the action track similarity condition with the target person;
determining a second monitoring point set of the target person based on the human body structural data of the target person and the monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set, wherein the determining comprises the following steps:
and determining a second monitoring point set of the target person based on the human body structural data of the target person, the human body structural data of the at least one peer person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set.
3. The method of claim 2, wherein the obtaining human body structural data of at least one peer person of the target person from the monitored images of the monitoring points in the first set of monitoring points comprises:
acquiring face image data shot by a plurality of monitoring points in the first monitoring point set and snapshot time corresponding to each piece of face image data;
determining face image data meeting the condition that the capturing time is close to the target figure face image data based on the acquired face image data and the capturing time corresponding to each piece of face image data;
among the determined face image data, dividing the face image data with the similarity larger than a second preset threshold into an image group;
determining at least one image group containing face image data of which the number is larger than a third preset threshold, and determining human body structural data corresponding to the face image data in the at least one image group as human body structural data of at least one same-line person of the target person.
4. The method of claim 2, wherein determining a second set of monitoring points to capture the target person based on the human structural data of the target person, the human structural data of the at least one peer person, and monitoring images captured by other monitoring points associated with the monitoring points in the first set of monitoring points comprises:
acquiring human body structural data of other monitoring points related to the monitoring point in the first monitoring point set;
determining all monitoring points of the other associated monitoring points which simultaneously satisfy at least the following two conditions as a second monitoring point set:
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the target person is greater than a preset value;
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the people in the same line is greater than a preset value.
5. The method according to any one of claims 1 to 4, wherein the other monitoring points associated with the monitoring point of the first set of monitoring points are monitoring points having a distance to any monitoring point of the first set of monitoring points smaller than a fourth preset threshold.
6. An apparatus for determining a trajectory of a target person, the apparatus comprising:
the determining module is used for determining a first monitoring point set which is captured to the target person based on the reference human face image data of the target person and the monitoring images shot by the monitoring points;
the acquisition module is used for acquiring the human body structural data of the target person, and the human body structural data of the target person is acquired according to the monitoring images shot by the monitoring points in the first monitoring point set;
the determining module is further configured to determine a second monitoring point set in which the target person is shot based on the human body structural data of the target person and monitoring images shot by other monitoring points associated with the monitoring points in the first monitoring point set; and determining the action track of the target person based on the information of the first monitoring point set and the information of the second monitoring point set.
7. The apparatus of claim 6, wherein the obtaining module is further configured to:
if the number of the monitoring points in the first monitoring point set is larger than a first preset threshold value, acquiring human body structural data of at least one peer person of the target person according to the monitoring image of the monitoring points in the first monitoring point set, wherein the first preset threshold value is larger than 1, and the peer person is a person meeting the action track similarity condition with the target person;
the determining module is configured to:
and determining a second monitoring point set of the target person based on the human body structural data of the target person, the human body structural data of the at least one peer person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set.
8. The apparatus of claim 7, wherein the obtaining module is configured to:
acquiring face image data shot by a plurality of monitoring points in the first monitoring point set and snapshot time corresponding to each piece of face image data;
determining face image data meeting the condition that the capturing time is close to the target figure face image data based on the acquired face image data and the capturing time corresponding to each piece of face image data;
among the determined face image data, dividing the face image data with the similarity larger than a second preset threshold into an image group;
determining at least one image group containing face image data of which the number is larger than a third preset threshold, and determining human body structural data corresponding to the face image data in the at least one image group as human body structural data of at least one same-line person of the target person.
9. The apparatus of claim 7, wherein the determining module is configured to:
acquiring human body structural data of other monitoring points related to the monitoring point in the first monitoring point set;
determining all monitoring points of the other associated monitoring points which simultaneously satisfy at least the following two conditions as a second monitoring point set:
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the target person is greater than a preset value;
the human body structural data of the monitoring point has human body structural data, the similarity of which with the human body structural data of the people in the same line is greater than a preset value.
10. The apparatus according to any one of claims 6-9, wherein the other monitoring points associated with the monitoring point in the first set of monitoring points are monitoring points having a distance to any monitoring point in the first set of monitoring points smaller than a fourth preset threshold.
11. A system for determining an action track of a target person, the system comprising a server and a plurality of monitoring points, wherein:
the monitoring point is used for shooting a monitoring image in a monitoring range and sending the shot monitoring image to the server;
the server is used for determining a first monitoring point set which is captured to the target person based on the reference human face imaging data of the target person and the monitoring images shot by the monitoring points; acquiring human body structural data of the target person, wherein the human body structural data of the target person is acquired according to monitoring images shot by monitoring points in the first monitoring point set; determining a second monitoring point set of the target person based on the human body structural data of the target person and monitoring images shot by other monitoring points related to the monitoring points in the first monitoring point set; and determining the action track of the target person based on the information of the first monitoring point set and the information of the second monitoring point set.
12. A server, comprising a processor and a memory, wherein the memory has stored therein at least one instruction, which is loaded and executed by the processor, to implement the method of determining a trajectory of action of a target person according to any one of claims 1 to 5.
13. A computer-readable storage medium having stored thereon at least one instruction, which is loaded and executed by a processor, for performing the method of determining a trajectory of an action of a target person according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910020875.0A CN111429476B (en) | 2019-01-09 | 2019-01-09 | Method and device for determining action track of target person |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910020875.0A CN111429476B (en) | 2019-01-09 | 2019-01-09 | Method and device for determining action track of target person |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111429476A true CN111429476A (en) | 2020-07-17 |
| CN111429476B CN111429476B (en) | 2023-10-20 |
Family
ID=71545914
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910020875.0A Active CN111429476B (en) | 2019-01-09 | 2019-01-09 | Method and device for determining action track of target person |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111429476B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112040186A (en) * | 2020-08-28 | 2020-12-04 | 北京市商汤科技开发有限公司 | Method, device and equipment for determining activity area of target object and storage medium |
| CN112149627A (en) * | 2020-10-19 | 2020-12-29 | 杭州海康威视数字技术股份有限公司 | Method and device for identifying fellow persons, electronic equipment and storage medium |
| CN112380901A (en) * | 2020-10-10 | 2021-02-19 | 杭州翔毅科技有限公司 | Behavior track generation method, behavior track generation equipment, storage medium and device |
| CN112417977A (en) * | 2020-10-26 | 2021-02-26 | 青岛聚好联科技有限公司 | Target object searching method and terminal |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140348382A1 (en) * | 2013-05-22 | 2014-11-27 | Hitachi, Ltd. | People counting device and people trajectory analysis device |
| US20150104066A1 (en) * | 2013-10-10 | 2015-04-16 | Canon Kabushiki Kaisha | Method for improving tracking in crowded situations using rival compensation |
| KR20150055271A (en) * | 2013-11-13 | 2015-05-21 | 현대모비스 주식회사 | Apparatus for determining motion characteristics of target and device for controlling driving route of vehicle with the said apparatus |
| CN104794458A (en) * | 2015-05-07 | 2015-07-22 | 北京丰华联合科技有限公司 | Fuzzy video person identifying method |
| CN105139040A (en) * | 2015-10-13 | 2015-12-09 | 商汤集团有限公司 | Queuing state information detection method and system thereof |
| CN106874347A (en) * | 2016-12-26 | 2017-06-20 | 深圳市深网视界科技有限公司 | A kind of method and system for matching characteristics of human body and MAC Address |
| US20170177947A1 (en) * | 2015-12-18 | 2017-06-22 | Canon Kabushiki Kaisha | Methods, devices and computer programs for tracking targets using independent tracking modules associated with cameras |
| CN107016374A (en) * | 2017-04-12 | 2017-08-04 | 电子科技大学 | Intelligent Measurement tracking and the generation method of space-time track towards specific objective |
| CN107292252A (en) * | 2017-06-09 | 2017-10-24 | 南京华捷艾米软件科技有限公司 | A kind of personal identification method of autonomous learning |
| CN107292240A (en) * | 2017-05-24 | 2017-10-24 | 深圳市深网视界科技有限公司 | It is a kind of that people's method and system are looked for based on face and human bioequivalence |
| CN107437075A (en) * | 2017-07-29 | 2017-12-05 | 安徽博威康信息技术有限公司 | A kind of risk alarm system based on daily behavior track |
| CN107480246A (en) * | 2017-08-10 | 2017-12-15 | 北京中航安通科技有限公司 | A kind of recognition methods of associate people 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 |
| CN108229335A (en) * | 2017-12-12 | 2018-06-29 | 深圳市商汤科技有限公司 | It is associated with face identification method and device, electronic equipment, storage medium, program |
| CN108875548A (en) * | 2018-04-18 | 2018-11-23 | 科大讯飞股份有限公司 | Personage's orbit generation method and device, storage medium, electronic equipment |
| CN108897777A (en) * | 2018-06-01 | 2018-11-27 | 深圳市商汤科技有限公司 | Target object method for tracing and device, electronic equipment and storage medium |
| CN109117714A (en) * | 2018-06-27 | 2019-01-01 | 北京旷视科技有限公司 | A kind of colleague's personal identification method, apparatus, system and computer storage medium |
-
2019
- 2019-01-09 CN CN201910020875.0A patent/CN111429476B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140348382A1 (en) * | 2013-05-22 | 2014-11-27 | Hitachi, Ltd. | People counting device and people trajectory analysis device |
| US20150104066A1 (en) * | 2013-10-10 | 2015-04-16 | Canon Kabushiki Kaisha | Method for improving tracking in crowded situations using rival compensation |
| KR20150055271A (en) * | 2013-11-13 | 2015-05-21 | 현대모비스 주식회사 | Apparatus for determining motion characteristics of target and device for controlling driving route of vehicle with the said apparatus |
| CN104794458A (en) * | 2015-05-07 | 2015-07-22 | 北京丰华联合科技有限公司 | Fuzzy video person identifying method |
| CN105139040A (en) * | 2015-10-13 | 2015-12-09 | 商汤集团有限公司 | Queuing state information detection method and system thereof |
| US20170177947A1 (en) * | 2015-12-18 | 2017-06-22 | Canon Kabushiki Kaisha | Methods, devices and computer programs for tracking targets using independent tracking modules associated with cameras |
| 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 |
| CN107527075A (en) * | 2016-06-20 | 2017-12-29 | 杭州海康威视数字技术股份有限公司 | RFID label tag is established with personnel's corresponding relation and trajectory track method and device |
| CN106874347A (en) * | 2016-12-26 | 2017-06-20 | 深圳市深网视界科技有限公司 | A kind of method and system for matching characteristics of human body and MAC Address |
| CN107016374A (en) * | 2017-04-12 | 2017-08-04 | 电子科技大学 | Intelligent Measurement tracking and the generation method of space-time track towards specific objective |
| CN107292240A (en) * | 2017-05-24 | 2017-10-24 | 深圳市深网视界科技有限公司 | It is a kind of that people's method and system are looked for based on face and human bioequivalence |
| CN107292252A (en) * | 2017-06-09 | 2017-10-24 | 南京华捷艾米软件科技有限公司 | A kind of personal identification method of autonomous learning |
| CN107437075A (en) * | 2017-07-29 | 2017-12-05 | 安徽博威康信息技术有限公司 | A kind of risk alarm system based on daily behavior track |
| CN107480246A (en) * | 2017-08-10 | 2017-12-15 | 北京中航安通科技有限公司 | A kind of recognition methods of associate people and device |
| CN108229335A (en) * | 2017-12-12 | 2018-06-29 | 深圳市商汤科技有限公司 | It is associated with face identification method and device, electronic equipment, storage medium, program |
| CN108875548A (en) * | 2018-04-18 | 2018-11-23 | 科大讯飞股份有限公司 | Personage's orbit generation method and device, storage medium, electronic equipment |
| CN108897777A (en) * | 2018-06-01 | 2018-11-27 | 深圳市商汤科技有限公司 | Target object method for tracing and device, electronic equipment and storage medium |
| CN109117714A (en) * | 2018-06-27 | 2019-01-01 | 北京旷视科技有限公司 | A kind of colleague's personal identification method, apparatus, system and computer storage medium |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112040186A (en) * | 2020-08-28 | 2020-12-04 | 北京市商汤科技开发有限公司 | Method, device and equipment for determining activity area of target object and storage medium |
| CN112380901A (en) * | 2020-10-10 | 2021-02-19 | 杭州翔毅科技有限公司 | Behavior track generation method, behavior track generation equipment, storage medium and device |
| CN112149627A (en) * | 2020-10-19 | 2020-12-29 | 杭州海康威视数字技术股份有限公司 | Method and device for identifying fellow persons, electronic equipment and storage medium |
| CN112417977A (en) * | 2020-10-26 | 2021-02-26 | 青岛聚好联科技有限公司 | Target object searching method and terminal |
| CN112417977B (en) * | 2020-10-26 | 2023-01-17 | 青岛聚好联科技有限公司 | Target object searching method and terminal |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111429476B (en) | 2023-10-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11354901B2 (en) | Activity recognition method and system | |
| US11200404B2 (en) | Feature point positioning method, storage medium, and computer device | |
| CN111429476B (en) | Method and device for determining action track of target person | |
| US20180061076A1 (en) | Fast multi-object detection and tracking system | |
| CN109426785B (en) | Human body target identity recognition method and device | |
| CN111950321B (en) | Gait recognition method, device, computer equipment and storage medium | |
| CN111814655B (en) | Target re-identification method, network training method thereof and related device | |
| US9323989B2 (en) | Tracking device | |
| CN111814690B (en) | Target re-identification method, device and computer readable storage medium | |
| CN113610967B (en) | Three-dimensional point detection method, three-dimensional point detection device, electronic equipment and storage medium | |
| CN111353429A (en) | Interest degree method and system based on eyeball turning | |
| CN114519863A (en) | Human body weight recognition method, human body weight recognition apparatus, computer device, and medium | |
| KR20140141239A (en) | Real Time Object Tracking Method and System using the Mean-shift Algorithm | |
| Tsai et al. | Joint detection, re-identification, and LSTM in multi-object tracking | |
| Sokolova et al. | Methods of gait recognition in video | |
| CN111860559A (en) | Image processing method, image processing device, electronic equipment and storage medium | |
| KR102465437B1 (en) | Apparatus and method for tracking object based on artificial intelligence | |
| CN114333039B (en) | Method, device and medium for clustering human images | |
| Chakraborty et al. | Person re-identification using multiple first-person-views on wearable devices | |
| CN112819859B (en) | Multi-target tracking method and device applied to intelligent security | |
| CN112257666B (en) | Target image content aggregation method, device, equipment and readable storage medium | |
| CN115035160A (en) | Target tracking method, device, equipment and medium based on visual following | |
| CN114387296A (en) | Target track tracking method and device, computer equipment and storage medium | |
| CN113297423A (en) | Pushing method, pushing device and electronic equipment | |
| CN114596638A (en) | Face living body detection method, device and storage medium |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |