WO2023072269A1 - Suivi d'objet - Google Patents

Suivi d'objet Download PDF

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Publication number
WO2023072269A1
WO2023072269A1 PCT/CN2022/128396 CN2022128396W WO2023072269A1 WO 2023072269 A1 WO2023072269 A1 WO 2023072269A1 CN 2022128396 W CN2022128396 W CN 2022128396W WO 2023072269 A1 WO2023072269 A1 WO 2023072269A1
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historical
current
neighbor
objects
node
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PCT/CN2022/128396
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English (en)
Chinese (zh)
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李经纬
王哲
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上海商汤智能科技有限公司
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Publication of WO2023072269A1 publication Critical patent/WO2023072269A1/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
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures

Definitions

  • This disclosure relates to the field of computer technology, and in particular, to object tracking.
  • Object tracking is a technology that determines the trajectory and state of motion of objects in images based on multiple frames of images, and can be applied to autonomous driving scenarios of intelligent driving devices (such as autonomous vehicles, vehicles equipped with assisted driving systems, robots, etc.).
  • intelligent driving devices such as autonomous vehicles, vehicles equipped with assisted driving systems, robots, etc.
  • high-precision object tracking is an important part of vehicle intelligence and automation, and is the basis of intelligent driving device perception, control, path planning and other modules.
  • the intelligent driving device can be equipped with image acquisition devices such as laser radar to locate the objects around it, and track the position of the recognized objects, correlate the continuous detection results in time series, and use the object tracking results to determine the movement of the objects
  • the trajectory estimates the motion state of the detected object, and then accurately predicts the driving route of the intelligent driving device.
  • Embodiments of the present disclosure at least provide an object tracking method, device electronic equipment, and a storage medium.
  • an embodiment of the present disclosure provides an object tracking method, including: acquiring the position information of multiple current objects detected in the current frame image, and obtaining the position information of multiple current objects detected in the history frame images before the current frame image position information of historical objects; wherein, the time interval between the acquisition of the historical frame image and the current frame image is less than or equal to a preset time threshold; for each historical object in the plurality of historical objects, based on the history The position information of the object in the historical frame image is used to generate the predicted position information of the historical object in the current frame image; for each current object in the plurality of current objects, based on the position information of the plurality of current objects The location information determines the neighbor topology graph of the current object, wherein the neighbor topology graph of the current object includes a first node representing the location characteristics of the current object, a second node representing the location characteristics of the neighbor objects of the current object, and the A connection edge between the first node and the second node; for each historical object in the plurality of historical objects, determine the
  • the embodiment of the present disclosure also provides an object tracking device, including: an acquisition module, configured to acquire the position information of multiple current objects detected in the current frame image, and the historical frame images before the current frame image The position information of a plurality of historical objects detected in; wherein, the time interval between the acquisition of the historical frame image and the current frame image is less than or equal to the preset time threshold; a generation module, for the plurality of historical objects For each historical object in, generate the predicted position information of the historical object in the current frame image based on the position information of the historical object in the historical frame image; the determination module is configured to target the plurality of current objects For each current object in , determine the neighbor topology map of the current object based on the position information of the multiple current objects, wherein the neighbor topology map of the current object includes a first node representing the position feature of the current object, representing the The second node of the position feature of the neighbor object of the current object and the connecting edge between the first node and the second node; and for each historical object in the plurality of
  • an embodiment of the present disclosure further provides an electronic device, including: a processor, a memory, and a bus, the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processing The processor communicates with the memory through a bus, and the machine-readable instructions execute the steps in the first aspect when executed by the processor.
  • the embodiments of the present disclosure further provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above-mentioned first aspect are executed.
  • FIG. 1 shows a flow chart of an object tracking method provided by an embodiment of the present disclosure
  • FIG. 2 shows a flow chart of generating a neighbor topology map provided by an embodiment of the present disclosure
  • Fig. 3 shows a schematic diagram of an object tracking device provided by an embodiment of the present disclosure
  • Fig. 4 shows a schematic diagram of an electronic device provided by an embodiment of the present disclosure.
  • a and/or B may mean that A exists alone, A and B exist simultaneously, or B exists alone.
  • at least one herein means any one of a variety or any combination of at least two of the more, for example, including at least one of A, B, and C, which may mean including from A, Any one or more elements selected from the set formed by B and C.
  • object tracking in dense scenes has the defect of low tracking accuracy.
  • special scenes such as dense crowds
  • there is a complex occlusion relationship between detected objects and it is easy to detect objects in different frames during object tracking.
  • the incorrect association of the objects resulting in the wrong results of object tracking, so that there is a certain risk in driving.
  • an embodiment of the present disclosure provides an object tracking method, device, electronic device, and computer-readable storage medium, using the location information of the current object and the historical object to determine the neighbor topology map of the current object and the historical object, and using the current object Object tracking can be performed on neighbor topology graphs of existing objects and neighbor topology graphs of historical objects to improve the accuracy of object tracking.
  • the embodiment of the present disclosure discloses an object tracking method, which can be applied to an electronic device with computing capabilities, such as a server.
  • the object tracking method may include the following steps:
  • the above-mentioned current frame image can be collected by an image acquisition device.
  • the image acquisition device can be a monocular camera, a multi-eye camera, a laser radar, an acoustic radar, etc., and the collected images can be point cloud data, depth images, ordinary images, etc. .
  • the above-mentioned image acquisition device can be deployed on an intelligent driving device.
  • the intelligent driving device can be a self-driving vehicle, a vehicle equipped with an auxiliary driving system, or a robot.
  • the image acquisition device in this embodiment takes a laser radar as an example, and the laser radar can acquire Point cloud data of surrounding objects, and determine the position information of each detected object according to the point cloud data.
  • the position information can also include the attitude information of the object, such as the length and width of the object , height, and the deflection angle of the object, etc.
  • the above-mentioned current frame image and the historical frame image may be two consecutive frames of images, that is, the historical frame image is the previous frame image of the current frame image, and the above-mentioned current object is an object that appears in the detection result of the current frame point cloud data, and the above-mentioned
  • the historical objects are objects that appear in the detection results of the historical frame point cloud data.
  • the historical frame image and the current frame image may also be two discontinuous frames, and the time interval between the two acquisitions is less than or equal to a preset threshold, so as to ensure that the two frames of images can be used for object tracking.
  • the same object may be detected in the current frame image and the historical frame image. For example, 60 objects are detected in the historical frame image, and 70 objects are detected in the current frame image. Among them, it is possible There are 40 objects detected in the historical frame image and the current frame image at the same time, 20 objects in the historical frame image are not detected in the current frame image, and 30 objects in the current frame image are not in the historical frame image.
  • its motion path can be determined according to its position information in the historical frame image and the current frame image, and the object can be tracked.
  • the position information of the above-mentioned historical objects is based on the position measurement when the image acquisition device collects the historical frame image, and cannot represent the position information of the historical object in the current frame image.
  • the position information of the historical object in the current frame image can be predicted by using the position information of the historical object to obtain predicted position information.
  • the above predicted position information is the prediction result of the historical object in the current frame image, which can be determined by using the motion state information of the historical object in the historical frame image and/or the motion state information of the image acquisition device.
  • the motion state information includes but not limited to position offset and the like.
  • the predicted position information of the above-mentioned historical objects in the current frame image may be generated through the following steps:
  • the position of the historical object is offset to obtain predicted position information of the historical object in the current frame image.
  • the coordinate system used by the lidar detection result is based on itself as the origin. Since the intelligent driving device is usually in motion, the image acquisition device on the intelligent driving device is also in motion, which leads to the detection of historical frame images The result is not consistent with the coordinate system of the detection result of the current frame image.
  • the position information when the current frame image is collected by the image acquisition device, and the position information when the historical frame image is collected can determine the position offset vector, thereby determining the offset of the coordinate system of the two frames of images, using the position offset vector (for example, a translation vector and/or a rotation vector) to offset the position of the historical object to obtain the predicted position information of the historical object in the current frame image.
  • the position offset vector for example, a translation vector and/or a rotation vector
  • the predicted position information of the historical object in the current frame image can be obtained.
  • each current object among the multiple current objects determine a neighbor topology map of the current object based on the location information of the multiple current objects, where the neighbor topology map of the current object includes a The first node of the position feature, the second node representing the position feature of the neighbor object of the current object, and the connection edge between the first node and the second node; and, for the plurality of historical objects
  • the above-mentioned neighbor topological graph can be composed of nodes and connection edges, and each object in the current object can have its corresponding neighbor topological graph, wherein, there is a first node in the neighbor topological graph of the current object, which is used to represent the location characteristics of the current object , the first node corresponds to at least one second node, and the second node is used to represent the location feature of the neighbor node corresponding to the first node.
  • the neighbor node of the node is the neighbor object of the object corresponding to the node.
  • the location feature can be Determined according to the location information corresponding to the node.
  • Each object in the historical object may have its corresponding neighbor topology graph, wherein, there is a third node in the neighbor topology graph of the historical object, which is used to represent the predicted location feature of the historical object, and the third node corresponds to at least one fourth node, The fourth node is used to represent the predicted location features of the neighbor objects of the historical object.
  • the following steps may be used to determine the neighbor topology map of the current object:
  • the neighbor objects of the current object can be determined according to the position information of multiple current objects. If it is determined that the Euclidean distance between the current object and the other object is less than or equal to a preset threshold, the other object may be used as a neighbor object of the current object.
  • the location characteristics of each current object can also be determined based on the location information of the current object. Specifically, the data of each dimension in the location information of the current object can be extracted to obtain the location characteristics of each current object. After that, it can be combined into An N-dimensional feature vector to get the feature vector corresponding to the position feature.
  • the position information includes coordinate information, size information and deflection angle
  • the position features may include x-axis features, y-axis features, z-axis features of the coordinate system, and length features length, width features width, height of the size information The characteristic height, and the deflection angle characteristic yaw.
  • the node corresponding to the current object may be generated based on the location characteristics of the current object, and then the corresponding nodes may be generated based on the location characteristics of the neighbor objects of the current object.
  • the location information of each node matches its corresponding location feature.
  • connection edges connecting each node can be generated.
  • the connection edges connect the first node with its corresponding second node, and there is no connection edge between the second nodes.
  • the connection edge It may be a directed edge from the first node to the second node. If a first node has K second nodes, its corresponding neighbor topology graph includes K+1 nodes and K connection edges.
  • This embodiment enables the neighbor topology map to reflect the location features of the current object and its neighbors, and uses the location features of the current object and its neighbors to determine the object tracking result, improving the accuracy of object tracking.
  • the location can be characterized as Indicates the center point coordinates, length, width, height and orientation angle of the i-th object;
  • the topological relationship between the 0th neighbor object and its neighbor objects in the collection is represented by a directed graph, and the neighbor topology graph is obtained, which is recorded as It has K+1 vertices and K directed edges, all of which are directed edges from the 0th neighbor node to other nodes, because the total number of nodes is K+1, so the number of edges is K. in is a collection of nodes, Represents the feature of the i kth vertex, the feature here is the position feature, which can be expressed by (x, y, z, l, w, h, yaw), or can be obtained from Extracted from the original point cloud data, it can also be a combination of the above two features.
  • (x, y, z, l, w, h, yaw) can be used as an example to describe.
  • the set of edges is defined as where f diff
  • f diff is a function of directed edges computed from nodes. Since nodes are generally N-dimensional vectors, for example, two vectors can be directly subtracted to obtain the vector f diffmitted of the connecting edge.
  • the neighbor topology graph of the historical object can be determined in a similar manner.
  • the flow chart of generating a neighbor topology map can first generate each node according to the location characteristics of each object, and determine the neighbor objects of each object respectively, according to the location characteristics of the neighbor objects and The location feature of the current object calculates the connection edge, and splices the generated nodes and the connection edge to obtain a neighbor topology map.
  • the current objects can be matched with historical objects, and the corresponding relationship between each current object and each historical object can be determined.
  • the corresponding relationship can be Including matching, adding and disappearing, among them, if a current object and a historical object are the same object, then the relationship between the current object and the historical object can be a match; if a current object and each historical object are not the same object, the current object is a newly added object, and its correspondence with the historical object can be newly added; if a historical object and each current object are not the same object, then the correspondence between the historical object and the current object can be disappear.
  • the tracking result of each object can be determined according to the determined corresponding relationship, and the object tracking result can be updated.
  • the similarity between the neighbor topological graph of each current object and the neighbor topological graph of each historical object may be determined; and then the object tracking result is updated based on the determined similarity.
  • the corresponding relationship between each current object and each historical object may be determined according to the similarity between each current object and the neighbor topology map of each historical object, and then the object tracking result is updated according to the determined corresponding relationship.
  • the similarity between the neighbor topological map of the current object and the neighbor topological map of each of the historical objects may be determined; based on the similarity, update the corresponding The object tracking results.
  • the following steps may be used to determine the similarity between the neighbor topology graph of each current object among the multiple current objects and the neighbor topology graph of each historical object:
  • the Euclidean distance between the current object and the historical object is less than or equal to a preset distance threshold, based on the current object's neighbor topology map and the history object's neighbor topology map, determining a first similarity between a node of the current object and a node of the historical object, and a second similarity between a node of a neighbor object of the current object and a node of a neighbor object of the historical object;
  • the position deviation between the position of each object in the current frame image and the position in the historical frame image is small, if a current object and a historical object For the same object, the similarity between its neighbor topological maps should be high, and the Euclidean distance between them should be small. If the Euclidean distance is greater than the preset distance threshold, it is considered that they are not the same object, and the similarity is set to 0, if the Euclidean distance is less than or equal to the preset distance threshold, it can be considered that the current object and the historical object may be in a matching relationship.
  • the node of the current object can be determined based on the neighbor topology map of the current object and the historical object
  • the feature vector difference between the position feature of the current object and the position feature of the historical object can be calculated, and the determined difference is modulo-processed, and then multiplied by -1 , get the first similarity
  • the second similarity between the nodes of the neighbor objects of the current object and the nodes of the neighbor objects of the history object may be determined according to the neighbor topology graph of the current object and the neighbor topology graph of the historical object.
  • the neighbor topology graph of the current object may be determined according to the neighbor topology graph of the current object and the neighbor topology graph of the historical object.
  • the similarity matrix determines the similarity matrix to obtain a neighbor similarity matrix neighbour_matrix of x*y, and each element in the neighbor similarity matrix is the node corresponding to the neighbor object of the current object and the neighbor object of the corresponding historical object
  • the third similarity between the nodes use the Hungarian matching algorithm or other matching algorithms to solve the neighbor similarity matrix, and get a set of optimal matching relationship neighbour_match, for example, you can take the pair of neighbors with the highest third similarity
  • the node is neighbour_match
  • neighbour_match includes the neighbor object of the current object and the neighbor object of the historical object matched with it, that is, to determine whether the neighbor objects of the current object and the neighbor objects of the historical object are the same object, after obtaining the neighbour_match , the third similarities in neighbour_match can be added to obtain the second similarity.
  • the first similarity and the second similarity can be added to obtain the i-th current object and the j-th historical object similarity_matrix(i,j) between the neighbor topological graphs of historical objects.
  • a similarity matrix similarity_matrix can be formed.
  • the similarity matrix By solving the similarity matrix, the corresponding relationship between each current object and each historical object can be obtained, and then, according to The determined correspondences update the object tracking results.
  • the obtained similarity matrix similarity_matrix can be solved by algorithms such as greedy nearest neighbor and Hungarian matching, so as to obtain the corresponding relationship between the current object and the historical object.
  • the specific method can be the same as the method for determining the neighbour_match.
  • the obtained corresponding relationship can be used as the matching result of the current object and the historical object.
  • different methods can be used to update the object tracking result.
  • the position information of the current object can be used to update the object tracking result corresponding to the historical object; is newly added), an object tracking result for the current object can be newly created, and the position information of the current object is used as its corresponding object tracking result; for each historical object in the plurality of historical objects, in response to determining that there is no
  • the current object matched by the historical object that is, the corresponding result is disappearing
  • Equal to the retention time threshold that is, whether the historical object has not been detected for a period that reaches the preset retention time threshold, so as to determine whether to retain or clear the object tracking result of the historical object.
  • the intelligent driving device loaded with the image acquisition device that collects the above-mentioned current frame image and historical frame image can be controlled based on the updated object tracking result, for example, there is a detected Adjust the driving route, driving speed, etc.
  • the location information of the current object and the location information of the historical object are used to determine the neighbor topology map of the current object and the neighbor topology map of the historical object, and the object tracking is carried out by using the neighbor topology map of the current object and the neighbor topology map of the historical object, and the object tracking is improved. Tracking accuracy.
  • the present disclosure also discloses an object tracking device, each module in the device can implement each step in the object tracking method of each of the above-mentioned embodiments, and can achieve the same beneficial effect, therefore, The same part will not be repeated here.
  • the object tracking device includes:
  • An acquisition module 310 configured to acquire position information of a plurality of current objects detected in the current frame image, and position information of a plurality of historical objects detected in a history frame image before the current frame image; wherein, the history The time interval between the frame image and the acquisition of the current frame image is less than or equal to a preset time threshold;
  • a generating module 320 configured to, for each of the multiple historical objects, generate predicted position information of the historical object in the current frame image based on the position information of the historical object in the historical frame image ;
  • the determining module 330 determines a neighbor topology map of the current object based on the location information of the multiple current objects, wherein the neighbor topology map of the current object includes The first node of the location feature of the object, the second node representing the location feature of the neighbor object of the current object, and the connection edge between the first node and the second node; and for the plurality of historical objects For each historical object, determine the neighbor topology graph of the historical object based on the predicted location information of the multiple historical objects, wherein the neighbor topology graph of the historical object includes a third node representing the predicted location feature of the historical object, representing The fourth node of the predicted location feature of the neighbor object of the historical object and the connecting edge between the third node and the fourth node;
  • An update module 340 configured to update object tracking results based on the neighbor topology graphs of the multiple current objects and the neighbor topology graphs of the multiple historical objects.
  • the generating module 320 is specifically configured to:
  • the position of the historical object in the historical frame image is offset to obtain the predicted position information of the historical object in the current frame image.
  • the determining module 330 determines, for each current object among the multiple current objects, the neighbor topology map of the current object based on the location information of the multiple current objects, Used for:
  • a connection edge connecting the first node and the second node is generated to obtain a neighbor topology graph of the current object.
  • the updating module 340 is specifically configured to:
  • For each current object determine the similarity between the neighbor topology map of the current object and the neighbor topology map of each of the historical objects;
  • the update module 340 determines the similarity between the current object's neighbor topological map and the neighbor topological maps of each of the historical objects, it is used to:
  • the Euclidean distance between the current object and the historical object is less than or equal to a preset distance threshold, based on the current object's neighbor topology map and the history object's neighbor topology map, determining a first similarity between a node of the current object and a node of the historical object, and a second similarity between a node of a neighbor object of the current object and a node of a neighbor object of the historical object;
  • the update module 340 determines the similarity between the current object's neighbor topological map and the neighbor topological maps of each of the historical objects, it is used to:
  • the updating module 340 determines the first relationship between the node of the current object and the node of the historical object based on the topology graph of the neighbors of the current object and the topology graph of the neighbors of the historical object.
  • similarity is used for:
  • the first similarity is determined based on a difference between a feature vector corresponding to the position feature of the current object and a feature vector corresponding to the position feature of the historical object.
  • the update module 340 determines the relationship between the node of the neighbor object of the current object and the neighbor object of the history object based on the neighbor topology graph of the current object and the neighbor topology graph of the historical object.
  • the second similarity of nodes is used for:
  • first neighbor object of the current object For a first neighbor object of the current object, based on the neighbor topology map of the current object and the neighbor topology map of the historical object, determine the third similarity between the first neighbor object and each neighbor object of the historical object degree; the first neighbor object is any one of the neighbor objects of the current object;
  • the second similarity between the node of the neighbor object of the current object and the node of the neighbor object of the history object is determined.
  • the updating module 340 when updating the object tracking result corresponding to the current object based on the similarity, the updating module 340 is configured to:
  • For the current object based on the similarity, determine a matching result between the current object and each of the historical objects;
  • an object tracking result corresponding to the historical object is updated using the location information of the current object.
  • the updating module 340 when updating the object tracking result corresponding to the current object based on the similarity, is further configured to:
  • an object tracking result corresponding to the current object is established using the location information of the current object.
  • the updating module 340 when updating the object tracking result corresponding to the current object based on the similarity, is further configured to:
  • each historical object in the plurality of historical objects in response to determining that there is no current object matching the historical object, based on the acquisition time and current time of the historical frame image corresponding to the historical object, determine to keep or clear the historical object The object tracking result for the object.
  • the device further includes a control module, configured to:
  • an intelligent driving device loaded with an image acquisition device for acquiring the current frame image and the historical frame image is controlled.
  • an embodiment of the present disclosure further provides an electronic device 400, as shown in FIG. 4 , which is a schematic structural diagram of the electronic device 400 provided by the embodiment of the present disclosure, including:
  • Memory 42 is used for storing execution order, comprises memory 421 and external memory 422; Memory 421 here is also called internal memory, is used for temporarily storing the operation data in processor 41, and with The data exchanged by the external memory 422 such as hard disk, the processor 41 exchanges data with the external memory 422 through the memory 421, when the electronic device 400 is running, the processor 41 communicates with the memory 42 through the bus 43, so that the processor 41 executes the following instructions :
  • each current object in the plurality of current objects determine a neighbor topology map of the current object based on the position information of the plurality of current objects, wherein the neighbor topology map of the current object includes a position characteristic representing the current object The first node of the current object, the second node representing the position characteristics of the neighbor objects of the current object, and the connection edge between the first node and the second node; and for each history object in the plurality of history objects object, determining a neighbor topology graph of the historical object based on the predicted location information of the multiple historical objects, wherein the neighbor topology graph of the historical object includes a third node representing the predicted location feature of the historical object, and a node representing the historical object A fourth node of the predicted location feature of the neighbor object and a connecting edge between the third node and the fourth node;
  • An object tracking result is updated based on the neighbor topology graphs of the multiple current objects and the neighbor topology graphs of the multiple historical objects.
  • Embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the steps of the object tracking method described in the foregoing method embodiments are executed.
  • the storage medium may be a volatile or non-volatile computer-readable storage medium.
  • the computer program product of the object tracking method provided by the embodiments of the present disclosure includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the steps of the object tracking method described in the above method embodiments
  • program code can be used to execute the steps of the object tracking method described in the above method embodiments
  • An embodiment of the present disclosure further provides a computer program, which implements any one of the methods in the preceding embodiments when the computer program is executed by a processor.
  • the computer program product can be specifically realized by means of 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.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor.
  • the technical solution of the present disclosure is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present disclosure.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disc and other media that can store program codes. .

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Abstract

La présente divulgation concerne un procédé et un appareil de suivi d'objet, ainsi qu'un dispositif électronique et un support de stockage. Le procédé consiste : à acquérir des informations de position d'une pluralité des objets actuels, qui sont détectés dans l'image de trame actuelle, et des informations de position d'une pluralité d'objets historiques, qui sont détectés dans une image de trame historique avant l'image de trame actuelle ; pour chaque objet historique parmi la pluralité d'objets historiques, à générer des informations de position prédites de l'objet historique dans l'image de trame actuelle sur la base des informations de position de l'objet historique dans l'image de trame historique ; pour chacun des objets actuels parmi la pluralité des objets actuels, à déterminer un graphe topologique voisin de l'objet actuel sur la base des informations de position de la pluralité des objets actuels, et, pour chaque objet historique parmi la pluralité d'objets historiques, à déterminer un graphe topologique voisin de l'objet historique sur la base des informations de position prédites de la pluralité d'objets historiques ; et à mettre à jour un résultat de suivi d'objet sur la base des graphes topologiques voisins de la pluralité des objets actuels et des graphes topologiques voisins de la pluralité d'objets historiques.
PCT/CN2022/128396 2021-10-29 2022-10-28 Suivi d'objet WO2023072269A1 (fr)

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CN202111271923.7 2021-10-29
CN202111271923.7A CN113971687A (zh) 2021-10-29 2021-10-29 对象跟踪方法、装置电子设备及存储介质

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CN113971687A (zh) * 2021-10-29 2022-01-25 上海商汤临港智能科技有限公司 对象跟踪方法、装置电子设备及存储介质

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