WO2022099988A1 - Procédé et appareil de suivi d'objet, dispositif électronique et support de stockage - Google Patents

Procédé et appareil de suivi d'objet, dispositif électronique et support de stockage Download PDF

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Publication number
WO2022099988A1
WO2022099988A1 PCT/CN2021/086020 CN2021086020W WO2022099988A1 WO 2022099988 A1 WO2022099988 A1 WO 2022099988A1 CN 2021086020 W CN2021086020 W CN 2021086020W WO 2022099988 A1 WO2022099988 A1 WO 2022099988A1
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Prior art keywords
target object
target
objects
camera
feature
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PCT/CN2021/086020
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English (en)
Chinese (zh)
Inventor
关英妲
周杨
刘文韬
钱晨
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北京市商汤科技开发有限公司
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Priority to JP2022506434A priority Critical patent/JP2022552772A/ja
Priority to KR1020227002278A priority patent/KR102446688B1/ko
Publication of WO2022099988A1 publication Critical patent/WO2022099988A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/292—Multi-camera tracking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G06T7/248—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments involving reference images or patches
    • 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

Definitions

  • the present disclosure relates to the technical field of computer vision, and in particular, to a target tracking method and device, an electronic device and a storage medium.
  • Object tracking refers to tracking the same target object (eg pedestrian, vehicle, etc.) in consecutive video frames.
  • target tracking algorithms are widely used in security and other fields, and are of great value in building a smart life.
  • ID identity document
  • the present disclosure provides a technical solution for target tracking.
  • a target tracking method comprising:
  • the distance between the target objects in the tracking area determine multiple target objects that conflict with each other;
  • a reference feature matching the current feature of the target object is determined, wherein the plurality of reference features are the corresponding reference features of the target objects before the plurality of target objects collide with each other. Describe the features extracted from multiple target objects;
  • the identification information corresponding to the reference feature matching the current feature of the target object is determined as the identification information of the target object.
  • the present disclosure by determining a plurality of target objects that conflict with each other according to the distance between the target objects in the tracking area, in response to any target object in the plurality of target objects and other target objects in the plurality of target objects When the target objects no longer conflict, extract the current feature of the target object, determine the reference feature matching the current feature of the target object based on the plurality of reference features corresponding to the plurality of target objects, and compare the reference feature with the target object's current features.
  • the identification information corresponding to the reference feature of the current feature matching is determined as the identification information of the target object, so that the consistency of the identification information of the target object can be maintained, that is, the consistency of the identification information of the same target object when entering and leaving the conflict area can be maintained , which can improve the accuracy of multi-target tracking.
  • the distance between any target object among the multiple target objects that conflict with each other and at least one other target object among the multiple target objects is less than or equal to a distance threshold
  • the extracting the current feature of the target object in response to the situation that any target object in the plurality of target objects is no longer in conflict with other target objects in the plurality of target objects includes:
  • the conflict relationship between target objects is determined, and target tracking is performed based on this, which helps to improve the accuracy and efficiency of target tracking.
  • the method further includes:
  • any target object determine the first coordinates of the target object corresponding to multiple cameras; wherein, the first coordinates of the target object corresponding to any camera are used to represent the first coordinates of the target object obtained according to the images collected by the camera. a coordinate;
  • the sub-area where the target object is located in the tracking area wherein the tracking area includes a plurality of sub-areas
  • the first coordinates of the target object corresponding to the multiple cameras are fused to obtain the fusion coordinates of the target object;
  • the method further includes:
  • the distance between the target objects in the tracking area is determined according to the fusion coordinates of the target objects in the tracking area.
  • the fusion coordinates of the target object are obtained, and the target tracking is performed based on the fusion coordinates of the target object, so that a more accurate
  • the coordinates are used for target tracking, which helps to improve the accuracy of target tracking.
  • the extracting the current feature of the target object includes:
  • Extract the current feature of the target object according to the video frame collected by the camera used for extracting the current feature of the target object.
  • the camera for extracting the current feature of the target object is determined according to the confidence of the plurality of cameras for the sub-region where the target object is located, and the camera for extracting the current feature of the target object is determined according to the camera
  • the current feature of the target object is extracted from the collected video frame, and the current feature of the target object thus extracted can have richer visual information. Therefore, performing target matching according to the current feature of the target object extracted by this implementation method can Improve the accuracy of target matching.
  • determining the camera used to extract the current feature of the target object according to the confidence of the plurality of cameras with respect to the sub-region where the target object is located includes:
  • the multiple cameras determine the The camera that extracts the current features of this target object.
  • the target object is located by the plurality of cameras, and the difference between the detection frame of the target object and the detection frames of other target objects in the video frames collected by the plurality of cameras.
  • the overlapping information of the target object is determined, the camera used for extracting the current feature of the target object is determined, and the current feature of the target object is extracted according to the video frame collected by the camera used for extracting the current feature of the target object, and the target object extracted from this
  • the current feature of the target object can have richer visual information, therefore, target matching is performed according to the current feature of the target object extracted in this implementation manner, which can further improve the accuracy of target matching.
  • the overlapping information includes the intersection ratio of the detection frame of the target object and the detection frames of other target objects;
  • the determination is based on the confidence of the multiple cameras for the sub-region where the target object is located, and the overlap information between the detection frame of the target object and the detection frames of other target objects in the video frames collected by the multiple cameras.
  • the camera used to extract the current features of the target object including:
  • the camera with the highest confidence for the sub-region where the target object is located is determined as the camera for extracting the current feature of the target object, wherein the overlapping condition indicates that the current acquisition
  • the intersection ratios of the detection frame of the target object and the detection frames of other target objects in the video frame of the target object are all smaller than a predetermined threshold.
  • the camera with the highest confidence in the sub-region where the target object is located is determined as the camera used for extracting the target object among the cameras whose currently collected video frames satisfy the overlapping condition.
  • the current feature of the camera, and the current feature of the target object is extracted according to the video frame collected by the camera used to extract the current feature of the target object.
  • the current feature of the target object thus extracted can have richer visual information. Therefore, , and perform target matching according to the current feature of the target object extracted by this implementation, which can further improve the accuracy of target matching.
  • the method further includes:
  • the confidence level of the camera for the plurality of sub-areas is determined.
  • the confidence of the camera for the plurality of sub-regions is determined according to the distance between the camera and the plurality of sub-regions of the tracking area, and based on the This determined confidence level performs coordinate fusion and/or determines the camera used to extract the current features of the target object, thereby helping to improve the accuracy of target tracking.
  • the method further includes:
  • the confidence level of the camera with respect to the plurality of sub-regions is adjusted.
  • any camera in the plurality of cameras according to the average distance between the target objects in the video frames collected by the camera, dynamically adjust the confidence level of the camera with respect to the plurality of sub-regions, and Coordinate fusion and/or determination of a camera for extracting current features of the target object is performed based on the confidence level thus determined, thereby helping to improve the accuracy of target tracking.
  • the determining a reference feature matching the current feature of the target object based on the plurality of reference features corresponding to the plurality of target objects includes:
  • a reference feature with the greatest similarity to the current feature of the target object is determined from a plurality of reference features corresponding to the plurality of target objects, and the reference feature with the highest similarity is responsive to the reference feature with the highest similarity with the target object.
  • the reference feature with the greatest similarity is determined as the reference feature matching the current feature of the target object, so that the target object and multiple
  • a reference feature matching the current feature of the target object is searched, thereby helping to improve the search efficiency of the target object.
  • the speed and accuracy of the current feature matching the benchmark feature is performed.
  • the method further includes:
  • the reference feature corresponding to the conflicting target object is determined, and the reference feature matching the current feature of the target object is determined, so that the determined reference feature can be further improved on the premise of improving the speed of finding the reference feature matching the current feature of the target object.
  • the accuracy of the fiducial feature matching the current features of this target object is determined, and the accuracy of the fiducial feature matching the current features of this target object.
  • the method further includes:
  • the last moment does not conflict with other target objects and does not conflict with all target objects.
  • the target object matched by other target objects at the current moment is determined to be the remaining target objects at the previous moment; situation, the target object in the conflict zone to which the target object belongs at the last moment, and the target object that does not match with other target objects at the current moment, is determined as the remaining target object at the last moment;
  • the target object with the closest distance to the target object at the current moment is determined as the target object matching the target object at the current moment, and the matching target
  • the identification information of the object is used as the identification information of the target object.
  • the amount of calculation and the time overhead can be reduced, and the real-time requirement of target tracking can be met.
  • a target tracking device comprising:
  • a first determining part configured to determine a plurality of target objects that conflict with each other according to the distance between the target objects in the tracking area
  • an extraction part configured to extract the current feature of the target object in response to a situation in which any one of the plurality of target objects is no longer in conflict with other target objects in the plurality of target objects;
  • the second determination part is configured to determine, based on a plurality of reference features corresponding to the plurality of target objects, a reference feature that matches the current feature of the target object, wherein the plurality of reference features are in the plurality of targets Before the objects collide with each other, the features extracted from the multiple target objects respectively;
  • the third determining part is configured to determine, as the identification information of the target object, the identification information corresponding to the reference feature matched with the current feature of the target object.
  • an electronic device comprising: one or more processors; a memory for storing executable instructions; wherein the one or more processors are configured to invoke the memory storage executable instructions to perform the above method.
  • a computer-readable storage medium having computer program instructions stored thereon, the computer program instructions implementing the above method when executed by a processor.
  • a computer program including computer readable code, which when executed in an electronic device, implements the above method when executed by a processor in the electronic device.
  • the present disclosure by determining a plurality of target objects that conflict with each other according to the distance between the target objects in the tracking area, in response to any target object in the plurality of target objects and other target objects in the plurality of target objects When the target objects no longer conflict, extract the current feature of the target object, determine the reference feature matching the current feature of the target object based on the plurality of reference features corresponding to the plurality of target objects, and compare the reference feature with the target object's current features.
  • the identification information corresponding to the reference feature of the current feature matching is determined as the identification information of the target object, so that the consistency of the identification information of the target object can be maintained, that is, the consistency of the identification information of the same target object when entering and leaving the conflict area can be maintained , which can improve the accuracy of multi-target tracking.
  • FIG. 1 shows a flowchart of a target tracking method provided by an embodiment of the present disclosure.
  • FIG. 2 shows a schematic diagram of a plurality of target objects colliding with each other at time t.
  • FIG. 3 shows a schematic diagram of a plurality of target objects colliding with each other at time t+1.
  • FIG. 4 shows a schematic diagram of multiple target objects and multiple reference features corresponding to the multiple target objects in an embodiment of the present disclosure.
  • FIG. 5 shows a schematic diagram of placing markers in a tracking area in an embodiment of the present disclosure.
  • FIG. 6 shows a block diagram of a target tracking apparatus provided by an embodiment of the present disclosure.
  • FIG. 7 shows a block diagram of an electronic device 800 provided by an embodiment of the present disclosure.
  • FIG. 8 shows a block diagram of an electronic device 1900 provided by an embodiment of the present disclosure.
  • FIG. 1 shows a flowchart of a target tracking method provided by an embodiment of the present disclosure.
  • the execution subject of the target tracking method may be a target tracking device.
  • the target tracking method may be performed by a terminal device or a server or other processing device.
  • the terminal device may be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, or a wearable devices, etc.
  • the target tracking method may be implemented by a processor invoking computer-readable instructions stored in a memory. As shown in FIG. 1 , the target tracking method includes steps S11 to S14.
  • step S11 according to the distance between the target objects in the tracking area, a plurality of target objects that conflict with each other are determined.
  • the embodiments of the present disclosure can be applied to application scenarios such as intelligent monitoring, intelligent scene analysis, security protection, target detection, and target tracking.
  • the tracking area represents an area where target tracking needs to be performed.
  • the tracking area in the embodiment of the present disclosure may be a relatively closed area or a relatively open area.
  • the tracking area may be a stadium, a mall, a classroom, and the like.
  • the target object represents the object that needs to be tracked.
  • the target object can be any object that needs to be tracked, such as a pedestrian, a vehicle, an athlete in a sports field (eg, a player in a football field).
  • target objects that conflict with each other may represent target objects that are closer to each other.
  • the distance between the target objects in the tracking area can be determined respectively, so as to determine the conflict between the target objects at the moment corresponding to the video frame.
  • the video frames collected by the camera may not be analyzed frame by frame.
  • the distance between the target objects in the tracking area may be determined every several video frames, so as to determine the distance between the target objects at the moment corresponding to the video frame. conflict situation.
  • FIG. 2 shows a schematic diagram of a plurality of target objects colliding with each other at time t. As shown in FIG. 2 , at time t, the target objects O 1 , O 4 and O 3 conflict with each other, the target objects O 2 and O 5 conflict with each other, and the target objects O 6 and O 7 conflict with each other.
  • multiple target objects that conflict with each other may be added to the same conflict area, that is, multiple target objects that conflict with each other may be considered to be in the same conflict area.
  • the conflict area may be a virtual area, and the target objects in any conflict area conflict with each other.
  • the conflict zone 1 includes target objects O 1 , O 4 and O 3
  • the conflict zone 2 includes target objects O 2 and O 5
  • the conflict zone 3 includes target objects O 6 and O 7 .
  • step S12 in response to the situation that any one of the multiple target objects is no longer in conflict with other target objects of the multiple target objects, the current feature of the target object is extracted.
  • FIG. 3 shows a schematic diagram of a plurality of target objects colliding with each other at time t+1.
  • conflict area 1 includes target objects O 1 and O 4
  • conflict area 2 includes target objects O 2 and O 5
  • conflict area 3 includes target objects O 6 and O 7 . That is, at time t+1, the target objects O 1 and O 4 conflict with each other, the target objects O 2 and O 5 conflict with each other, and the target objects O 6 and O 7 conflict with each other.
  • the target object O 3 is no longer in conflict with O 1 and O 4 ; that is, all the target objects in the scene converge dynamically at any time. Classes into multiple conflict zones.
  • the time interval between two adjacent moments may be equal to the inverse of the frame rate at which the video frame is captured by the camera.
  • the time interval between two adjacent moments may be greater than the inverse of the frame rate at which the camera captures video frames, for example, it may be equal to H times the inverse of the frame rate at which the camera captures video frames, wherein , H is an integer greater than 1.
  • the distance between any target object among the multiple target objects that conflict with each other and at least one other target object among the multiple target objects is less than or equal to a distance threshold; the response In a situation where any one of the multiple target objects is no longer in conflict with other target objects in the multiple target objects, extracting the current feature of the target object includes: for any one of the multiple target objects The target object, in response to the situation that the distances between the target object and other target objects in the plurality of target objects are all greater than the distance threshold, extract the current feature of the target object.
  • a noise-based density clustering method (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) can be used to cluster target objects whose distances from other target objects are less than a distance threshold to form conflict zone.
  • DBSCAN Density-Based Spatial Clustering of Applications with Noise
  • the distance between any two target objects can be calculated, and if the distance between the two target objects is less than or equal to a distance threshold, it can be determined that the two target objects conflict with each other. If the distance between another target object and at least one of the two target objects is less than or equal to the distance threshold, it may be determined that the three target objects conflict with each other. For example, at time t, if the distance between the target object O1 and the target object O4 is less than or equal to the distance threshold, it can be determined that the target object O1 and the target object O4 conflict. If the distance between the target object O 3 and the target object O 4 is less than or equal to the distance threshold, it can be determined that the target objects O 1 , O 4 and O 3 conflict with each other.
  • the target objects when judging whether the target objects conflict with each other, it may only consider whether the distance between the positions of the target objects (for example, fusion coordinates) is less than or equal to the distance threshold, for example, if any two target objects If the distance between the positions is less than or equal to the distance threshold, it can be determined that the two target objects collide with each other.
  • the distance between the positions of the target objects for example, fusion coordinates
  • the distance between the positions of the target objects may be considered, for example, if any two targets If the distance between the positions of the objects is less than or equal to the first distance threshold, and the distance between the visual features of the two target objects is less than or equal to the second distance threshold, it can be determined that the two target objects conflict with each other.
  • the distance between the visual features of the target object may represent the similarity between the visual features of the target object.
  • the conflict relationship between target objects is determined, and target tracking is performed based on this, which helps to improve the accuracy and efficiency of target tracking.
  • the distance between any target object among the multiple target objects that conflict with each other and all other target objects among the multiple target objects is less than or equal to a distance threshold; the In response to a situation in which any one of the plurality of target objects no longer conflicts with other target objects in the plurality of target objects, extracting the current feature of the target object includes: for any one of the plurality of target objects; A target object, when the distance between the target object and any target object among the plurality of target objects is greater than the distance threshold, extract the current feature of the target object.
  • the extracting the current feature of the target object includes: determining a sub-area where the target object is located in the tracking area; Determine the camera used for extracting the current feature of the target object; according to the video frame collected by the camera used for extracting the current feature of the target object, extract the current feature of the target object.
  • the tracking area may include at least one sub-area, for example, the tracking area may be divided into a plurality of sub-areas.
  • the tracking area may be divided into a plurality of sub-areas.
  • the camera for extracting the current feature of the target object is determined according to the confidence of the plurality of cameras for the sub-region where the target object is located, and the camera for extracting the current feature of the target object is determined according to the camera
  • the current feature of the target object is extracted from the collected video frame, and the current feature of the target object thus extracted can have richer visual information. Therefore, performing target matching according to the current feature of the target object extracted by this implementation method can Improve the accuracy of target matching.
  • the determining a camera for extracting the current feature of the target object according to the confidence of the sub-region where the target object is located by the plurality of cameras includes: according to the plurality of cameras for the target object The confidence level of the sub-region where it is located, and the overlapping information between the detection frame of the target object and the detection frames of other target objects in the video frames collected by the multiple cameras, determine the method used to extract the current feature of the target object. Camera.
  • the confidence of the sub-region where the target object is located by the plurality of cameras, and the difference between the detection frame of the target object and the detection frames of other target objects in the video frames collected by the plurality of cameras Overlapping information, determine the camera used for extracting the current feature of the target object, and extract the current feature of the target object according to the video frame collected by the camera used for extracting the current feature of the target object, and extract the current feature of the target object.
  • the current feature can have richer visual information, therefore, performing target matching according to the current feature of the target object extracted from this example can further improve the accuracy of target matching.
  • the overlapping information includes the intersection ratio of the detection frame of the target object and the detection frames of other target objects; the confidence level of the sub-region where the target object is located according to the plurality of cameras, and the The overlapping information between the detection frame of the target object and the detection frames of other target objects in the video frames collected by multiple cameras, and determining the camera used to extract the current feature of the target object, including: from the cameras that meet the overlapping conditions, The camera with the highest confidence for the sub-region where the target object is located is determined as the camera used for extracting the current feature of the target object, wherein the overlapping condition indicates that the target object is in the currently collected video frame.
  • the intersection ratios of the detection frame and detection frames of other target objects are all smaller than a predetermined threshold.
  • the sub-area where the target object is located is the sub-area A 1
  • the multiple cameras include a camera C 1 , a camera C 2 , a camera C 3 and a camera C 4
  • the confidence level of the camera C 1 for the sub-area A 1 is Z 11.
  • the confidence level of the camera C2 for the sub-region A1 is Z 21
  • the confidence level of the camera C3 for the sub - region A 1 is Z 31
  • the confidence level of the camera C 4 for the sub -region A 1 is Z 41
  • the target object If there is no other detection frame whose intersection ratio with the detection frame of the target object is greater than or equal to a predetermined threshold in the video frame currently collected by the camera C1 , that is, in the video frame currently collected by the camera C1 , the target object If the intersection ratio between the detection frame of the object and the detection frames of other target objects is smaller than the predetermined threshold, the current feature of the target object is extracted according to the video frame collected by the camera C1 . If in the video frame currently collected by the camera C1 , there are other detection frames whose intersection ratio with the detection frame of the target object is greater than or equal to a predetermined threshold, then it is judged whether there is a video frame currently collected by the camera C2 that is related to the target object.
  • intersection ratio of the detection frame of the target object is greater than or equal to other detection frames with a predetermined threshold, that is, to determine whether the intersection ratio of the detection frame of the target object and the detection frames of other target objects in the video frame currently collected by the camera C 2 is not. are less than the predetermined threshold.
  • the target object If there is no other detection frame whose intersection ratio with the detection frame of the target object is greater than or equal to a predetermined threshold in the video frame currently collected by the camera C2, that is, in the video frame currently collected by the camera C2 , the target object The intersection ratio of the detection frame of the target object and the detection frame of other target objects is smaller than the predetermined threshold, then according to the video frame collected by the camera C 2 , the current feature of the target object is extracted, and so on; In the video frame currently collected by the camera with high confidence, if the target object is occluded, you can switch to the camera with the next highest confidence, and so on; Under the condition that the camera is not blocked, it is a camera with high confidence as much as possible, which reduces the missed detection rate and improves the accuracy of target matching.
  • the intersection ratio of the detection frame of the target object and the detection frames of other target objects in the video frame currently collected by the camera can indicate that the target object is affected by other target objects.
  • the proportion of target object occlusion The larger the intersection ratio between the detection frame of the target object and the detection frames of other target objects in the video frame currently collected by the camera, the greater the proportion of the target object being occluded by other target objects; the video currently collected by the camera is larger. The smaller the intersection ratio of the detection frame of the target object and the detection frames of other target objects in the frame, the smaller the proportion of the target object being occluded by other target objects.
  • the camera with the highest confidence for the sub-region where the target object is located among the cameras whose currently collected video frames meet the overlapping condition, is determined as the camera for extracting the current feature of the target object , and extract the current feature of the target object according to the video frame collected by the camera used to extract the current feature of the target object, so that the current feature of the target object extracted can have richer visual information. Therefore, according to this example The extracted current features of the target object are used for target matching, which can further improve the accuracy of target matching.
  • step S13 based on a plurality of reference features corresponding to the plurality of target objects, a reference feature matching the current feature of the target object is determined, wherein the plurality of reference features are when the plurality of target objects conflict with each other Before, the features extracted for the multiple target objects are respectively.
  • FIG. 4 shows a schematic diagram of multiple target objects and multiple reference features corresponding to the multiple target objects in an embodiment of the present disclosure.
  • the plurality of target objects include target object O 1 , target object O 2 , target object O 3 and target object O 4 , wherein the reference feature of target object O 1 is reference feature F 01 , and the target object The reference feature of O 2 is the reference feature F 02 , the reference feature of the target object O 3 is the reference feature F 03 , and the reference feature of the target object O 4 is the reference feature F 04 .
  • the reference feature of the target object may be extracted in the first frame, and the reference feature of the target object may be stored.
  • the fiducial features of all target objects can be extracted in the first frame, and the fiducial features of all target objects can be stored.
  • the reference features of these target objects can be extracted in subsequent video frames to obtain higher-quality reference features containing richer visual information of the target objects.
  • the correspondence between the identification information of the target object and the reference feature of the target object may be stored.
  • the manner of determining the camera for extracting the reference feature of the target object is similar to the manner in which the camera for extracting the current feature of the target object is determined above, and details are not described herein again.
  • the current feature of the target object can be updated according to the extracted current feature of the target object.
  • the fiducial features of the target object to improve the accuracy of target matching based on the stored fiducial features.
  • the stored reference feature of the target object may be weighted with the current feature of the target object to obtain a new reference feature.
  • the reference feature of the target object may not be updated to reduce the amount of computation.
  • the feature of the target object can be extracted by a ReID (person Re-IDentificaion, pedestrian re-identification) module.
  • a ReID person Re-IDentificaion, pedestrian re-identification
  • the reference feature and/or the current feature of the target object can be extracted by the ReID module.
  • the ReID module can be implemented using a convolutional neural network.
  • other feature extraction methods can also be used to extract the features of the target object, as long as the extracted features of the target object can reflect the visual information of the target object.
  • the determining, based on a plurality of reference features corresponding to the plurality of target objects, a reference feature that matches the current feature of the target object includes: selecting from a plurality of reference features corresponding to the plurality of target objects Among the reference features, the reference feature with the greatest similarity with the current feature of the target object is determined; in response to the situation that the similarity between the reference feature with the greatest similarity and the current feature of the target object is greater than or equal to the similarity threshold , and the reference feature with the greatest similarity is determined as the reference feature matching the current feature of the target object.
  • the similarity may be cosine similarity or the like.
  • the conflict area 1 includes target objects O 1 , O 4 and O 3
  • the reference features corresponding to the conflict area 1 include the reference feature F 01 of the target object O 1 and the reference feature F 04 of the target object O 4 and the reference feature F 03 of the target object O 3 .
  • the reference feature F 03 has the greatest similarity with the current feature F 1n of the target object, and the similarity between the reference feature F 03 and the current feature F 1n of the target object is greater than or equal to similarity If the degree threshold is set, the reference feature F 03 can be determined as the reference feature matching the current feature of the target object.
  • a reference feature with the greatest similarity to the current feature of the target object is determined from a plurality of reference features corresponding to the plurality of target objects, and the reference feature with the highest similarity is responsive to the reference feature with the highest similarity with the target object.
  • the reference feature with the greatest similarity is determined as the reference feature matching the current feature of the target object, so that the target object and multiple
  • a reference feature matching the current feature of the target object is searched, thereby helping to improve the search efficiency of the target object.
  • the speed and accuracy of the current feature matching the benchmark feature is performed.
  • the method further includes: in response to the reference feature with the greatest similarity and the current feature of the target object When the similarity between them is less than the similarity threshold, based on the reference feature corresponding to the target object that does not conflict with the target object and conflicts with any other target object, the reference feature matching the current feature of the target object is determined.
  • the reference features corresponding to other conflict areas (that is, not The reference feature corresponding to the target object that conflicts with the target object but conflicts with other target objects) is searched for the reference feature that matches the current feature of the target object. For example, if the similarity between the multiple reference features corresponding to the multiple target objects and the current feature of the target object is less than the similarity threshold, it may be determined that among the multiple reference features corresponding to the multiple target objects, find the There are no fiducial features that match the current features of this target object.
  • the reference feature corresponding to the target object is determined, and the reference feature matching the current feature of the target object is determined, so as to improve the speed of finding the reference feature matching the current feature of the target object.
  • a reference feature matching the current feature of the target object cannot be found in all conflict regions, a reference feature matching the current feature of the target object can be searched in the non-conflict region.
  • the determining, based on the plurality of reference features corresponding to the plurality of target objects, the reference features matching the current features of the target objects includes: determining the reference features corresponding to the plurality of target objects. Among the plurality of reference features, the reference feature with the greatest similarity with the current feature of the target object; the reference feature with the greatest similarity is determined as the reference feature matched with the current feature of the target object.
  • step S14 the identification information corresponding to the reference feature matching the current feature of the target object is determined as the identification information of the target object.
  • the identification information of the target object may be information that can be used to uniquely identify the target object, for example, may be an ID, a serial number, a name, and the like. For example, if the tracking area is a football field and the target object includes players on the football field, the identification information of the target object may be the team to which the player belongs and the jersey number.
  • O 3 may be determined as the identification information of the target object.
  • the target tracking result corresponding to any target object may also include position information of the target object.
  • the behavior of the target object in the tracking area can be analyzed. For example, if the tracking area is a football field, and the target object includes players on the football field, then according to the identification information of the players (such as the team and jersey numbers) and the position information of the players in at least one video frame, the behavior of the players can be determined. Analysis, such as analyzing whether the player is offside, etc.
  • the present disclosure by determining a plurality of target objects that conflict with each other according to the distance between the target objects in the tracking area, in response to any target object in the plurality of target objects and other target objects in the plurality of target objects When the target objects no longer conflict, extract the current feature of the target object, determine the reference feature matching the current feature of the target object based on the plurality of reference features corresponding to the plurality of target objects, and compare the reference feature with the target object's current features.
  • the identification information corresponding to the reference feature of the current feature matching is determined as the identification information of the target object, so that the consistency of the identification information of the target object can be maintained, that is, the consistency of the identification information of the same target object when entering and leaving the conflict area can be maintained , which can improve the accuracy of multi-target tracking.
  • the embodiments of the present disclosure can be applied to complex target tracking scenarios, for example, the target objects in the tracking area have different sizes, there are small-sized target objects in the tracking area that are difficult to detect, the target objects in the tracking area are dense, and the target objects are too small to be detected. Target tracking scenarios such as similar appearance, high motion complexity of the target object, and severe occlusion.
  • the method further includes: acquiring the first coordinates of the marker; for any camera in the plurality of cameras, obtaining the marker's coordinates according to the camera The second coordinate, and the first coordinate of the marker, determine the transformation matrix corresponding to the camera.
  • markers when calibrating the plurality of cameras, markers may be placed in the tracking area at a certain density first.
  • FIG. 5 shows a schematic diagram of placing markers in a tracking area in an embodiment of the present disclosure.
  • the tracking area is a football field and the marker is a white metal sheet.
  • markers placed according to a certain density can be of different heights, different sizes, and different colors, which are not limited here.
  • the first coordinates may be coordinates in a world coordinate system
  • the second coordinates may be coordinates in a pixel coordinate system
  • the transformation matrix may be a homography matrix. Since the target object moves in the same plane, formula (1) can be used to determine the transformation matrix H 3 ⁇ 3 :
  • (X w , Y ⁇ ) represents the first coordinate of the marker, for example, it can be the coordinate of the marker in the world coordinate system;
  • (u, v) represents the second coordinate of the marker obtained by the camera, for example, it can be the marker The coordinates of the object in the pixel coordinate system.
  • the degree of freedom of the homography matrix H 3 ⁇ 3 is 8, and theoretically, the homography matrix can be solved by using the feature points corresponding to 4 markers.
  • the least squares method can be used, outliers can be filtered, and the homography matrix can be obtained by using the checkerboard calibration method.
  • the homography matrix thus obtained can remove the distortion in the image captured by the camera, and can improve the accuracy of the determined first coordinate.
  • the camera external parameters can be obtained through Direct Linear Transformation (DLT) and Singular Value Decomposition (SVD), and the camera internal parameters can be obtained through checkerboard calibration to remove image distortion.
  • DLT Direct Linear Transformation
  • Singular Value Decomposition Singular Value Decomposition
  • the second coordinates of the target object obtained by the multiple cameras can be transformed into a unified coordinate system, for example, by converting In the world coordinate system, the first coordinates of the target object corresponding to each of the plurality of cameras are obtained, thereby facilitating subsequent tracking of the target object.
  • the method further includes: for any target object, determining that the target object corresponds to the first coordinates of the plurality of cameras; according to the first coordinates of the target object corresponding to the plurality of cameras , determine the sub-area where the target object is located in the tracking area, wherein the tracking area includes multiple sub-areas; The object is fused corresponding to the first coordinates of the plurality of cameras, and the fusion coordinates of the target object are obtained; before the plurality of conflicting target objects are determined according to the distance between the target objects in the tracking area, the method further includes: The distance between the target objects in the tracking area is determined according to the fusion coordinates of the target objects in the tracking area.
  • any target object corresponds to the first coordinates of any camera, and may represent the first coordinates of the target object obtained according to the images collected by the camera.
  • the first coordinates may be coordinates in a first coordinate system, for example, the first coordinate system may be a world coordinate system or other virtual coordinate systems.
  • the fusion coordinates and the first coordinates may be coordinates in the same coordinate system, for example, the fusion coordinates and the first coordinates may both be coordinates in the world coordinate system.
  • the coordinate range of each sub-area in the tracking area in the first coordinate system is predetermined.
  • the coordinate range of any sub-region in the first coordinate system can be represented by the coordinates of the four vertices of the sub-region in the first coordinate system, or, the sub-region in the first coordinate system
  • the coordinate range below can be represented by the coordinates of the top-left corner vertex of the sub-region in the first coordinate system and the width and height of the sub-region.
  • the coordinate range of any sub-region under the first coordinate system may also be represented by other manners, which is not limited here.
  • the shape of any sub-region may not be a rectangle, for example, a triangle or the like. Different sub-areas in the tracking area can be the same size or different.
  • the sub-region where the target object is located in the tracking region can be determined .
  • determining the sub-area where the target object is located in the tracking area according to the first coordinates of the target object corresponding to the plurality of cameras includes: according to the target object corresponding to the The first coordinates of any camera in the plurality of cameras, and the coordinate range of the sub-region in the first coordinate system, determine the candidate sub-region where the target object is located in the tracking region; the candidate sub-region with the highest number of votes is determined.
  • the area is determined as the sub-area where the target object is located.
  • the plurality of cameras include a camera C 1 , a camera C 2 , a camera C 3 and a camera C 4 ; according to the first coordinate of the target object corresponding to the camera C 1 , determine where the target object is located in the tracking area
  • the candidate sub-region is sub-region A 1 ; according to the first coordinate of the target object corresponding to the camera C 2 , it is determined that the candidate sub-region where the target object is located in the tracking region is sub-region A 1 ; according to the target object Corresponding to the first coordinate of the camera C3, it is determined that the candidate sub-region where the target object is located in the tracking area is the sub-region A2 ; according to the first coordinate of the target object corresponding to the camera C4 , determine the target object
  • the candidate sub-area located in the tracking area is the sub-area A 1 ; then, the number of votes for the candidate sub-area A 1 is 3, and the number of votes for the candidate sub-area A 2 is 1. Therefore, the candidate sub-area A can be 1 is determined
  • determining the sub-area where the target object is located in the tracking area according to the first coordinates of the target object corresponding to the plurality of cameras includes: according to the target object corresponding to the The first coordinates of the multiple cameras are determined, and the average value of the first coordinates of the target object corresponding to the multiple cameras is determined; according to the coordinate range of the sub-region in the first coordinate system, the average value of the first coordinates is determined The sub-area where the target object is located, and the sub-area where the average value of the first coordinates is located is determined as the sub-area where the target object is located in the tracking area.
  • the first coordinates of the target object corresponding to all or part of the cameras in the plurality of cameras may be fused according to the confidence of the plurality of cameras with respect to the sub-region where the target object is located, Get the fusion coordinates of the target object.
  • the multiple cameras include camera C 1 , camera C 2 , camera C 3 and camera C 4 , the target object is located in sub-region A 1 , the confidence level of camera C 1 for sub-region A 1 is Z 11 , and camera C 2
  • the confidence level for sub-region A 1 is Z 21
  • the confidence level for camera C 3 for sub-region A 1 is Z 31
  • the confidence level for camera C 4 for sub-region A 1 is Z 41
  • the target object corresponds to camera C
  • the first coordinate of 1 is P w1
  • the first coordinate of the target object corresponding to the camera C 2 is P w2
  • the first coordinate of the target object corresponding to the camera C 3 is P w3
  • the first coordinate is P w4
  • the fusion coordinate of the target object can be
  • a part of the cameras with higher confidence in the sub-region where the target object is located may be selected to determine the fusion coordinates of the target object.
  • Z 11 the confidence level of camera C 1 for
  • the fusion coordinates of the target object are obtained, and the target tracking is performed based on the fusion coordinates of the target object, so that a more accurate
  • the coordinates are used for target tracking, which helps to improve the accuracy of target tracking.
  • the target is determined according to the second coordinates of the target object obtained by the camera and the transformation matrix corresponding to the camera The object corresponds to the camera's first coordinates.
  • the second coordinate of the target object obtained by the camera may be the coordinates of the target object in a pixel coordinate system corresponding to the camera. That is, the second coordinates may represent coordinates in the pixel coordinate system.
  • the target object corresponds to the first coordinates of the camera, and represents the coordinates of the target object in the world coordinate system determined according to the second coordinates of the target object obtained by the camera.
  • the transformation matrix corresponding to the camera may be a homography matrix.
  • the second coordinate of the target object obtained by the camera may also be the coordinates of the target object in a coordinate system such as an image coordinate system corresponding to the camera. That is, the second coordinates may also be coordinates in a coordinate system such as an image coordinate system.
  • target detection may be performed on an image collected by the camera, a detection frame of the target object in the image may be determined, and according to the position of the detection frame, the second coordinates of the target object obtained by the camera may be determined .
  • any point on the detection frame or any point inside the detection frame may be used as the second coordinate of the target object.
  • the midpoint of the bottom edge of the detection frame can be used as the second coordinate of the target object.
  • the transformation matrix corresponding to any camera in the plurality of cameras is H
  • the second coordinate of any target object obtained by the camera is P u
  • the first coordinate of the target object corresponding to the camera can use the formula (2) Determine:
  • the method further includes: for any camera in the plurality of cameras, according to the distance between the camera and the plurality of sub-regions of the tracking area, determine that the camera is aimed at the tracking area. Confidence for multiple subregions.
  • the confidence levels of the multiple cameras with respect to the multiple sub-areas may be determined according to the distances between the multiple cameras and the multiple sub-areas of the tracking area.
  • the confidence of any camera with respect to any sub-region is negatively correlated with the distance between the camera and the sub-region. That is, the larger the distance between the camera and the sub-area, the lower the confidence of the camera in the sub-area; the smaller the distance between the camera and the sub-area, the lower the confidence of the camera in the sub-area higher degree.
  • the confidence Z 11 of the camera C 1 for the sub-area A 1 may be greater than that of the camera C 2 Confidence Z 21 for sub-region A 1 .
  • the confidence Z 11 of the camera C 1 for the sub-area A 1 may be greater than that of the camera C 1 Confidence Z 12 for sub-region A 2 .
  • the confidence level of the camera for the plurality of sub-areas is determined according to the distance between the camera and the plurality of sub-areas of the tracking area, The distance between the camera and the sub-area is thus taken into consideration for the confidence of the camera for the sub-area. Coordinate fusion and/or determination of a camera for extracting current features of the target object is performed based on the confidence level thus determined, thereby helping to improve the accuracy of target tracking.
  • the method further includes: for any camera in the plurality of cameras, according to the camera The average distance between the target objects in the collected video frames is used to adjust the confidence of the camera for the multiple sub-regions.
  • the confidence of the camera for the multiple sub-regions may be continuously adjusted according to the average distance between the target objects in the video frames collected by the camera.
  • the confidence of the camera with respect to the plurality of sub-regions may be adjusted at a preset frequency.
  • the preset frequency may be 1 second.
  • a weighting process may be performed on the confidences of multiple cameras with respect to the sub-region where the target object is located, and then according to the weighted confidences of the multiple cameras with respect to the sub-region where the target object is located, The first coordinates of the target object corresponding to the multiple cameras are fused.
  • the confidence level of the camera with respect to the plurality of sub-regions is adjusted.
  • the multiple cameras include a camera C 1 , a camera C 2 , a camera C 3 and a camera C 4 , the average distance between target objects in the video frames collected by the camera C 1 is D 1 , and the video frames collected by the camera C 2
  • the average distance between the target objects is D 2
  • the average distance between the target objects in the video frame collected by the camera C 3 is D 3
  • the average distance between the target objects in the video frame collected by the camera C 4 is D 4
  • D 1 >D 2 >D 3 >D 4 the confidence of the camera C 1 for each of the sub-regions in the plurality of sub-regions can be increased by B 1
  • the camera C 2 for each of the sub-regions in the plurality of sub-regions.
  • B 2 The confidence of the region is increased by B 2
  • B 3 the confidence of the camera C 3 for each of the sub-regions is increased by B 3
  • B 3 the confidence of the camera C 4 for each of the sub-regions is kept unchanged.
  • the smaller the average distance between the target objects in the video frame collected by the camera the smaller the average distance between the target objects in the video frame collected by the camera.
  • the larger the average distance between the target objects in the video frames collected by the camera the less likely the target objects in the video frames collected by the camera are occluded from each other, which is conducive to the feature extraction of the target objects. This facilitates target tracking.
  • any camera in the plurality of cameras according to the average distance between the target objects in the video frames collected by the camera, dynamically adjust the confidence level of the camera with respect to the plurality of sub-regions, and Coordinate fusion and/or determination of a camera for extracting current features of the target object is performed based on the confidence level thus determined, thereby helping to improve the accuracy of target tracking.
  • the method further includes: for any target object, in response to the situation that the target object does not conflict with other target objects at the current moment and the previous moment of the current moment
  • the target object that does not conflict with other target objects at the last moment and does not match other target objects at the current moment is determined as the remaining target objects at the last moment; or, in response to the target object being in the current moment
  • the target object that does not match with other target objects at the current time in the conflict zone to which the target object belongs to the previous time is determined as The remaining target objects at the last moment; from the remaining target objects at the last moment, the target object with the closest distance to the target object at the current moment is determined to match the target object at the current moment
  • the target object, and the identification information of the matching target object is used as the identification information of the target object.
  • a certain target object does not conflict with other target objects at the current moment and the last moment of the current moment, and there are 15 target objects that do not conflict with other target objects at the last moment, which are target objects 0 1 to O 15 .
  • the target objects O 1 and O 2 in the previous moment have been matched with other target objects at the current moment, that is, the identification information O 1 and O 2 have been used as the identification information of the target objects at the current moment
  • the target object O 3 To O 15 is determined as the remaining target object at the previous moment.
  • the target object with the closest distance to the target object at the current moment is determined as the target object at the current moment.
  • the target object that matches this target object For example, if among the remaining target objects O 3 to O 15 at the previous moment, O 6 is the closest to the target object at the current moment, then the target object O 6 at the previous moment is determined to match the target object at the current moment. the target object, and the identification information of the target object at the current moment is determined as O 6 .
  • a certain target object conflicts with the same other target objects at the current moment and the last moment.
  • the conflict area to which the target object belongs at the last moment includes target object O 1 , target object O 2 , The target object O 3 and the target object O 4 . If in the conflict zone to which the target object belongs at the last moment, the target object O1 and the target object O2 have been matched with other target objects at the current moment, that is, the identification information O1 and O2 have been used as the current moment's The identification information of the target object, then the target object O 3 and the target object O 4 can be determined as the remaining target objects at the previous moment.
  • the target object with the closest distance to the target object at the current moment is determined as the target object with the current moment.
  • the target object that matches this target object For example, if among the remaining target objects O 3 and O 4 at the previous moment, O 4 is the closest to the target object at the current moment, then the target object O 4 at the previous moment is determined to match the target object at the current moment. the target object, and the identification information of the target object at the current moment is determined as O 4 .
  • the target object does not conflict with other target objects at the current moment and the previous moment of the current moment, or the target object is at the current moment and the previous moment.
  • the target object with the closest distance to the target object at the current moment is determined as the target object at the current moment.
  • the matching target object, and the identification information of the matching target object is used as the identification information of the target object, thereby adopting the distance-based greedy algorithm to determine the identification information of the target object, regardless of the visual feature of the target object, Thereby, the amount of calculation can be reduced, the time overhead can be reduced, and the real-time requirements of target tracking can be met.
  • the target tracking method provided by the embodiment of the present disclosure may be implemented by using a target tracking model.
  • the target tracking model may adopt Faster-RCNN (Faster Recurrent Convolutional Neural Network, faster recurrent convolutional neural network) or Fast-RCNN (Faster Recurrent Convolutional Neural Network, fast recursive convolutional neural network) or the like.
  • the backbone network of the target tracking model can adopt structures such as ResNet-18.
  • the target tracking model can be compressed by channel pruning, and at the same time, the multi-layer feature detection method is used to improve the model accuracy, so that the model accuracy is not reduced at the same time. , to increase model speed.
  • the method may further include: outputting the correspondence between the identification information of the target object and the coordinates.
  • the correspondence between the identification information and the coordinates may be output in the order of the identification information. For example, if the identification information includes the jersey numbers 1-11, the coordinates of the corresponding target objects can be output according to the order of the jersey numbers from small to large.
  • the motion trajectory of the target object may be obtained according to the coordinates of the target object at multiple times (for example, multiple consecutive times).
  • a Kalman filter algorithm may also be used to process the motion trajectory of the target object, so as to make the motion trajectory of the target object smoother.
  • the writing order of each step does not mean a strict execution order but constitutes any limitation on the implementation process, and the specific execution order of each step should be based on its function and possible Internal logic is determined.
  • the present disclosure also provides target tracking devices, electronic devices, computer-readable storage media, and programs, all of which can be used to implement any target tracking method provided by the present disclosure.
  • target tracking devices electronic devices, computer-readable storage media, and programs, all of which can be used to implement any target tracking method provided by the present disclosure.
  • FIG. 6 shows a block diagram of a target tracking apparatus provided by an embodiment of the present disclosure.
  • the target tracking device includes:
  • the first determining part 61 is configured to determine a plurality of target objects that conflict with each other according to the distance between the target objects in the tracking area;
  • the extraction part 62 is configured to extract the current feature of the target object in response to the situation that any target object in the plurality of target objects is no longer in conflict with other target objects in the plurality of target objects;
  • the second determining part 63 is configured to determine, based on a plurality of reference features corresponding to the plurality of target objects, a reference feature matching the current feature of the target object, wherein the plurality of reference features are in the plurality of target objects. Before the target objects collide with each other, the features extracted from the multiple target objects respectively;
  • the third determining part 64 is configured to determine, as the identification information of the target object, the identification information corresponding to the reference feature matching the current feature of the target object.
  • the distance between any target object among the multiple target objects that conflict with each other and at least one other target object among the multiple target objects is less than or equal to a distance threshold
  • the extraction section 62 is configured to:
  • the apparatus further includes:
  • the third determination part 64 is configured to, for any target object, determine the first coordinates of the target object corresponding to the plurality of cameras; wherein, the first coordinates of the target object corresponding to any camera are used to represent the acquisition according to the camera.
  • the first coordinate of the target object obtained from the image of ;
  • a fourth determining part configured to determine a sub-area where the target object is located in the tracking area according to the first coordinates of the target object corresponding to the plurality of cameras, wherein the tracking area includes a plurality of sub-areas ;
  • the fusion part is configured to fuse the first coordinates of the target object corresponding to the plurality of cameras according to the confidence of the plurality of cameras for the sub-region where the target object is located to obtain the fusion coordinates of the target object ;
  • the fifth determination part is configured to determine the distance between the target objects in the tracking area according to the fusion coordinates of the target objects in the tracking area.
  • the extraction part 62 is configured as:
  • Extract the current feature of the target object according to the video frame collected by the camera used for extracting the current feature of the target object.
  • the extraction part 62 is configured as:
  • the multiple cameras determine the The camera that extracts the current features of this target object.
  • the overlapping information includes the intersection ratio of the detection frame of the target object and the detection frames of other target objects;
  • the extraction section 62 is configured to:
  • the camera with the highest confidence for the sub-region where the target object is located is determined as the camera for extracting the current feature of the target object, wherein the overlapping condition indicates that the current acquisition
  • the intersection ratios of the detection frame of the target object and the detection frames of other target objects in the video frame of the target object are all smaller than a predetermined threshold.
  • the apparatus further includes:
  • the sixth determining part is configured to, for any one of the plurality of cameras, determine the confidence of the camera for the plurality of sub-areas according to the distance between the camera and the plurality of sub-areas of the tracking area.
  • the apparatus further includes:
  • the adjusting part is configured to, for any one of the plurality of cameras, adjust the confidence of the camera for the plurality of sub-regions according to the average distance between the target objects in the video frames collected by the camera.
  • the second determining part 63 is configured as:
  • the apparatus further includes:
  • a seventh determination part configured to respond to a situation where the similarity between the reference feature with the greatest similarity and the current feature of the target object is less than the similarity threshold, based on not conflicting with the target object and with any other target
  • the reference feature corresponding to the target object of the object conflict is determined, and the reference feature matching the current feature of the target object is determined.
  • the apparatus further includes:
  • the eighth determination part is configured to, for any target object, in response to the situation that the target object does not conflict with other target objects at the current moment and the previous moment of the current moment, the last moment does not conflict with other target objects.
  • the target object that conflicts with the target object and does not match with other target objects at the current moment is determined as the remaining target object at the previous moment; or, in response to the target object at the current moment and the previous moment In the case of conflict with the same other target object, the target object in the conflict zone to which the target object belongs at the last moment and which does not match with other target objects at the current moment is determined as the remaining target object at the last moment. target;
  • the ninth determination part is configured to, from the remaining target objects at the last moment, determine the target object with the closest distance to the target object at the current moment as the target matching the target object at the current moment object, and use the identification information of the matched target object as the identification information of the target object.
  • a "part" may be a part of a circuit, a part of a processor, a part of a program or software, etc., of course, a unit, a module or a non-modularity.
  • the present disclosure by determining a plurality of target objects that conflict with each other according to the distance between the target objects in the tracking area, in response to any target object in the plurality of target objects and other target objects in the plurality of target objects When the target objects no longer conflict, extract the current feature of the target object, determine the reference feature matching the current feature of the target object based on the plurality of reference features corresponding to the plurality of target objects, and compare the reference feature with the target object's current features.
  • the identification information corresponding to the reference feature of the current feature matching is determined as the identification information of the target object, so that the consistency of the identification information of the target object can be maintained, that is, the consistency of the identification information of the same target object when entering and leaving the conflict area can be maintained , which can improve the accuracy of multi-target tracking.
  • the functions or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the methods described in the above method embodiments, and the implementation of the above method embodiments may refer to the descriptions of the above method embodiments. Repeat.
  • Embodiments of the present disclosure further provide a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the foregoing method is implemented.
  • the computer-readable storage medium may be a non-volatile computer-readable storage medium, or may be a volatile computer-readable storage medium.
  • Embodiments of the present disclosure also provide a computer program product, including computer-readable codes.
  • a processor in the device executes a method for implementing the target tracking method provided in any of the above embodiments. instruction.
  • Embodiments of the present disclosure further provide another computer program product for storing computer-readable instructions, which, when executed, cause the computer to perform the operations of the target tracking method provided by any of the foregoing embodiments.
  • Embodiments of the present disclosure further provide an electronic device, including: one or more processors; a memory for storing executable instructions; wherein the one or more processors are configured to invoke executable instructions stored in the memory instruction to execute the above method.
  • the electronic device may be provided as a terminal, server or other form of device.
  • FIG. 7 shows a block diagram of an electronic device 800 provided by an embodiment of the present disclosure.
  • electronic device 800 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc. terminal.
  • an electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814 , and the communication component 816 .
  • the processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations.
  • the processing component 802 can include one or more processors 820 to execute instructions to perform all or some of the steps of the methods described above.
  • processing component 802 may include one or more modules that facilitate interaction between processing component 802 and other components.
  • processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.
  • Memory 804 is configured to store various types of data to support operation at electronic device 800 . Examples of such data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, and the like. Memory 804 may be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read only memory
  • EPROM erasable Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic or Optical Disk Magnetic Disk
  • Power supply assembly 806 provides power to various components of electronic device 800 .
  • Power supply components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800 .
  • Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user.
  • the touch panel includes one or more touch sensors to sense touch, swipe, and gestures on the touch panel. The touch sensor may not only sense the boundaries of a touch or swipe action, but also detect the duration and pressure associated with the touch or swipe action.
  • the multimedia component 808 includes a front-facing camera and/or a rear-facing camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each of the front and rear cameras can be a fixed optical lens system or have focal length and optical zoom capability.
  • Audio component 810 is configured to output and/or input audio signals.
  • audio component 810 includes a microphone (MIC) that is configured to receive external audio signals when electronic device 800 is in operating modes, such as calling mode, recording mode, and voice recognition mode.
  • the received audio signal may be further stored in memory 804 or transmitted via communication component 816 .
  • audio component 810 also includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which may be a keyboard, a click wheel, a button, or the like. These buttons may include, but are not limited to: home button, volume buttons, start button, and lock button.
  • Sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of electronic device 800 .
  • the sensor assembly 814 can detect the on/off state of the electronic device 800, the relative positioning of the components, such as the display and the keypad of the electronic device 800, the sensor assembly 814 can also detect the electronic device 800 or one of the electronic device 800 Changes in the position of components, presence or absence of user contact with the electronic device 800 , orientation or acceleration/deceleration of the electronic device 800 and changes in the temperature of the electronic device 800 .
  • Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact.
  • Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices.
  • the electronic device 800 may access wireless networks based on communication standards, such as Wi-Fi, 2G, 3G, 4G/LTE, 5G, or a combination thereof.
  • the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module may be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • electronic device 800 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A programmed gate array (FPGA), controller, microcontroller, microprocessor or other electronic component implementation is used to perform the above method.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGA field programmable A programmed gate array
  • controller microcontroller, microprocessor or other electronic component implementation is used to perform the above method.
  • a non-volatile computer-readable storage medium such as a memory 804 comprising computer program instructions executable by the processor 820 of the electronic device 800 to perform the above method is also provided.
  • FIG. 8 shows a block diagram of an electronic device 1900 provided by an embodiment of the present disclosure.
  • the electronic device 1900 may be provided as a server.
  • electronic device 1900 includes processing component 1922, which further includes one or more processors, and a memory resource represented by memory 1932 for storing instructions executable by processing component 1922, such as applications.
  • An application program stored in memory 1932 may include one or more modules, each corresponding to a set of instructions.
  • the processing component 1922 is configured to execute instructions to perform the above-described methods.
  • the electronic device 1900 may also include a power supply assembly 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input output (I/O) interface 1958 .
  • Electronic device 1900 may operate based on an operating system stored in memory 1932, such as Windows Mac OS or similar.
  • a non-volatile computer-readable storage medium such as memory 1932 comprising computer program instructions executable by processing component 1922 of electronic device 1900 to perform the above-described method.
  • the present disclosure may be a system, method and/or computer program product.
  • the computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the present disclosure.
  • a computer-readable storage medium may be a tangible device that can hold and store instructions for use by the instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Non-exhaustive list of computer readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM) or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically coded devices, such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above.
  • RAM random access memory
  • ROM read only memory
  • EPROM erasable programmable read only memory
  • flash memory static random access memory
  • SRAM static random access memory
  • CD-ROM compact disk read only memory
  • DVD digital versatile disk
  • memory sticks floppy disks
  • mechanically coded devices such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above.
  • Computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (eg, light pulses through fiber optic cables), or through electrical wires transmitted electrical signals.
  • the computer readable program instructions described herein may be downloaded to various computing/processing devices from a computer readable storage medium, or to an external computer or external storage device over a network such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • a network adapter card or network interface in each computing/processing device receives computer-readable program instructions from a network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .
  • Computer program instructions for carrying out operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or instructions in one or more programming languages.
  • Source or object code written in any combination, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as the "C" language or similar programming languages.
  • the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through the Internet connect).
  • LAN local area network
  • WAN wide area network
  • custom electronic circuits such as programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) can be personalized by utilizing state information of computer readable program instructions.
  • Computer readable program instructions are executed to implement various aspects of the present disclosure.
  • These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer or other programmable data processing apparatus to produce a machine that causes the instructions when executed by the processor of the computer or other programmable data processing apparatus , resulting in means for implementing the functions/acts specified in one or more blocks of the flowchart and/or block diagrams.
  • These computer readable program instructions can also be stored in a computer readable storage medium, these instructions cause a computer, programmable data processing apparatus and/or other equipment to operate in a specific manner, so that the computer readable medium on which the instructions are stored includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks of the flowchart and/or block diagrams.
  • Computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other equipment to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , thereby causing instructions executing on a computer, other programmable data processing apparatus, or other device to implement the functions/acts specified in one or more blocks of the flowcharts and/or block diagrams.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more functions for implementing the specified logical function(s) executable instructions.
  • the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions.
  • the computer program product can be specifically implemented by hardware, software or a combination thereof.
  • the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), etc. Wait.
  • a software development kit Software Development Kit, SDK
  • the current feature of the target object is extracted, and based on the multiple reference features corresponding to the multiple target objects, the reference feature matching the current feature of the target object is determined, and the reference feature matching the current feature of the target object is determined.
  • the identification information corresponding to the reference feature of the feature matching is determined as the identification information of the target object, so that the consistency of the identification information of the target object can be maintained, that is, the consistency of the identification information of the same target object when entering and leaving the conflict area can be maintained, Thus, the accuracy of multi-target tracking can be improved.

Abstract

La présente divulgation concerne un procédé et un appareil de suivi d'objet, un dispositif électronique et un support de stockage. Le procédé comprend les étapes consistant à : déterminer, en fonction de distances entre des objets cibles dans une zone de suivi, une pluralité d'objets cibles qui sont en conflit les uns avec les autres ; en réponse à une situation dans laquelle l'un quelconque de la pluralité d'objets cibles n'est plus en conflit avec les autres objets cibles dans la pluralité d'objets cibles, extraire la caractéristique actuelle de l'objet cible ; déterminer, sur la base d'une pluralité de caractéristiques de référence correspondant à la pluralité d'objets cibles, une caractéristique de référence correspondant à la caractéristique actuelle de l'objet cible, la pluralité de caractéristiques de référence étant des caractéristiques extraites respectivement pour la pluralité d'objets cibles avant que la pluralité d'objets cibles entrent en conflit les uns avec les autres ; et déterminer des informations d'identification correspondant à la caractéristique de référence correspondant à la caractéristique actuelle de l'objet cible en tant qu'informations d'identification de l'objet cible.
PCT/CN2021/086020 2020-11-11 2021-04-08 Procédé et appareil de suivi d'objet, dispositif électronique et support de stockage WO2022099988A1 (fr)

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JP2022506434A JP2022552772A (ja) 2020-11-11 2021-04-08 目標追跡方法及び装置、電子デバイスと記憶媒体
KR1020227002278A KR102446688B1 (ko) 2020-11-11 2021-04-08 타깃 추적 방법 및 장치, 전자 기기 및 저장 매체

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CN112330717A (zh) 2021-02-05

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