WO2020014901A1 - Procédé et appareil de suivi de cible, dispositif électronique et support de stockage lisible - Google Patents

Procédé et appareil de suivi de cible, dispositif électronique et support de stockage lisible Download PDF

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Publication number
WO2020014901A1
WO2020014901A1 PCT/CN2018/096161 CN2018096161W WO2020014901A1 WO 2020014901 A1 WO2020014901 A1 WO 2020014901A1 CN 2018096161 W CN2018096161 W CN 2018096161W WO 2020014901 A1 WO2020014901 A1 WO 2020014901A1
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Prior art keywords
video image
image
target
level
resolution
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PCT/CN2018/096161
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English (en)
Chinese (zh)
Inventor
杨文超
王恺
廉士国
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深圳前海达闼云端智能科技有限公司
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Priority to CN201880001302.7A priority Critical patent/CN109074657B/zh
Priority to PCT/CN2018/096161 priority patent/WO2020014901A1/fr
Publication of WO2020014901A1 publication Critical patent/WO2020014901A1/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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • 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 application relates to the field of computer vision, and in particular, to a method, an apparatus, an electronic device, and a readable storage medium for tracking an object.
  • Augmented Reality is a technology that calculates the position and angle of the camera image in real time and adds the corresponding image.
  • the goal of this technology is to put the virtual world on the screen and perform interactive.
  • Target detection and target tracking are key technologies in augmented reality.
  • Target detection can detect the precise position of the template map (flat target) in the video image, but the calculation of target detection is very time-consuming.
  • the initial position of the template image in the video image is usually obtained through target detection, and the precise position of the template image in the video image is subsequently determined by the target tracking method.
  • the general process of the target tracking method is: extract the feature points of the template map and search for the feature points in the image to be matched to obtain the homography matrix from the template map to the image to be matched, so as to determine that the template map is to be matched. Precise position in the image; according to the determined precise position and the historical position of the template image, predict the position of the template image in the next image to be matched, and continue to track the template image according to the predicted position.
  • the inventors discovered during the research of the prior art that currently, the following two methods are usually used for tracking a high-resolution image of a planar target: First, a large search radius is used to search for feature points of a planar target, but this method is time-consuming Serious; Second, the method uses a small search radius to search for feature points of a planar target, but this method is prone to search failures, which leads to the failure of tracking flat targets.
  • the plane target is tracked in real time, if the plane target moves relatively fast relative to the camera, it will often lead to the loss of tracking of the plane target or the phenomenon of jitter in the superimposed image, which reduces the User experience on AR.
  • a technical problem to be solved in some embodiments of the present application is to provide a method, an apparatus, an electronic device, and a readable storage medium for target tracking, so that when a target in a video image is tracked in real time, the target can be quickly and accurately realized.
  • the positioning position in the video image improves the user's experience of AR.
  • An embodiment of the present application provides a target tracking method, including: acquiring a frame of video image and acquiring a predicted position of the target in the video image; acquiring a reduced template image of the target; and according to the reduced template image and the predicted position, Determine the reduction ratio of the video image, and reduce the video image according to the reduction ratio to obtain a reduced video image; determine the predicted position of the target in the reduced video image according to the predicted position; use the predicted position of the target in the reduced video image, use The reduced template image is matched with the reduced video image to determine the rough positioning position information of the reduced template image in the reduced video image; the precise positioning position of the target in the video image is determined based on the rough positioning position information.
  • An embodiment of the present application further provides a target tracking device, including: a first acquisition module, a second acquisition module, an image reduction module, a predicted position reduction module, a coarse positioning module, and an accurate positioning module; the first acquisition module is used for Acquire a frame of video image, and obtain the predicted position of the target in the video image; the second acquisition module is used to acquire the reduced template image of the target; the image reduction module is used to determine the video image based on the reduced template image and the predicted position.
  • the prediction position reduction module is used to determine the predicted position of the target in the reduced video image according to the predicted position
  • the coarse positioning module is used to determine the target in the reduced video
  • the predicted position in the image is matched with the reduced template image and the reduced video image to determine the information of the rough positioning position of the reduced template image in the reduced video image
  • the precise positioning module is configured to determine the position based on the rough positioning Information to determine the target in the video map The precise positioning location.
  • An embodiment of the present application further provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are at least One processor executes to enable at least one processor to perform the above-mentioned target tracking method.
  • An embodiment of the present application further provides a computer-readable storage medium storing a computer program, which is implemented by the processor to implement the foregoing target tracking method.
  • the target template image and the obtained video image are reduced, and the resolution of the template image and the video image is greatly reduced, so that the template image and the video image are being processed.
  • it can quickly determine the coarse positioning position information of the reduced template image in the reduced video image; the coarse positioning position information makes the process of determining the precise positioning position of the target in the video image reduced.
  • Searching the scope of the template image in the video image greatly improves the speed of determining the precise positioning position and shortens the time to determine the precise positioning position of the target in the video image; and in the case of an error in the predicted position, due to the reduced template
  • the low resolution and small image size of images and reduced video images make it possible to quickly determine the information of the rough positioning position without changing the scope of the search template image, so as not to lose the target template image, which improves the Accuracy of target tracking to improve user experience on AR .
  • FIG. 1 is a specific flowchart of a target tracking method in the first embodiment of the present application
  • FIG. 2 is a schematic flowchart of a specific process for determining a precise positioning position of a target in a video image in a target tracking method according to a second embodiment of the present application;
  • FIG. 3 is a detailed flowchart of sub-pixel processing in a target tracking method in a third embodiment of the present application.
  • FIG. 4 is a schematic diagram of a specific structure of a target tracking device in a fourth embodiment of the present application.
  • FIG. 5 is a detailed structural diagram of an electronic device in a fifth embodiment of the present application.
  • the first embodiment of the present application relates to a target tracking method.
  • the target tracking method is suitable for a terminal, especially a mobile terminal, such as a smart phone, a smart tablet, and the like.
  • the specific process of the target tracking method is shown in Figure 1:
  • Step 101 Obtain a frame of video image, and obtain a predicted position of a target in the video image.
  • the video image is acquired by the terminal through a camera, for example, a smartphone is acquired by a camera, and a head-mounted device is acquired by a built-in camera or an external camera.
  • the target tracking in AR is achieved by tracking the targets in continuous frame images.
  • a target in a video image refers to a two-dimensional object in the video image (such as an image of a potted plant, an image of a lamp).
  • the way to obtain the predicted position of the target in the video image can be obtained by the target in the previous frame of the video image. Obtained from the position estimation; of course, the predicted position can also be obtained by a detection method. It should be noted that, for the first frame image, the predicted position of the target in the video image is obtained by a detection method, and the detection method is not described in detail in this embodiment.
  • Step 102 Obtain a reduced template image of the target.
  • the reduced template image is a reduced template image of a pre-stored target; or, the reduced template image is obtained by reducing the template image of the target according to a preset resolution.
  • the template image of the target refers to a two-dimensional image of the target.
  • the template image of a target may be an image of a lamp, an image of a pot, and the like.
  • the template image can be obtained from a cloud server, or it can be acquired by the terminal itself.
  • the method of obtaining the template image is not limited, and can be selected according to actual needs.
  • the reduced template image may be stored in the terminal in a fixed resolution in advance. Since the reduced template image is directly obtained, the speed of acquiring the reduced template image is greatly mentioned.
  • the full-resolution template image may be reduced according to a preset resolution.
  • the preset resolution is 25 ⁇ 25 pixels
  • the full resolution of the template image is 720 ⁇ 720. Pixels, the template image at full frequency is reduced to 25 ⁇ 25 pixels to obtain a reduced template image.
  • Step 103 Determine a reduction ratio of the video image according to the reduced template image and the predicted position, and reduce the video image according to the reduction ratio to obtain a reduced video image.
  • the estimated pixel area of the template image in the video image is determined according to the predicted position; the reduction ratio of the video image is determined according to the pixel area and the estimated pixel area of the reduced template image.
  • the predicted position is the predicted position of the template image in the video image.
  • the estimated pixel area of the template image in the video image can be calculated, and the pixel area of the reduced template image is calculated.
  • the reduction ratio of the video image is equal to The pixel area of the reduced template image divided by the square root of the quotient of the estimated pixel area.
  • the pixel area of the reduced template image is sm
  • the estimated pixel area of the full-resolution template image in the video image is sf.
  • the reduction ratio t is equal to the square root of (sm / sf).
  • the method of calculating the reduction ratio is not limited in this embodiment.
  • Step 104 Determine the predicted position of the target in the reduced video image according to the predicted position.
  • the predicted position of the target in the video image is reduced according to the reduction ratio of the reduced video image to obtain the predicted position of the target in the reduced video image.
  • Step 105 According to the predicted position of the target in the reduced video image, use the reduced template image to match the reduced video image, and determine information about the rough positioning position of the reduced template image in the reduced video image.
  • the reduced video image is searched for image blocks that match the reduced template image, and the rough positioning position information is determined according to the matched image blocks.
  • feature points are extracted from the reduced template image, and image blocks to be matched on the reduced template image are determined according to the feature points, and then the predicted position of the reduced template image in the reduced video image is used as a starting point.
  • search for the image block matching the reduced template image in the reduced video image with a preset radius can be determined according to the resolution of the reduced video image, which is not limited in this embodiment.
  • the matched image block in the reduced video image is an image containing the reduced template image feature points, and the size of the image block is the same as the size of the image block to be matched on the reduced template image.
  • the image blocks found in the reduced video image that match the reduced template image are the same image as the image blocks in the template image by default, so according to the feature points in the searched image block and the corresponding features in the reduced template image Point to determine the homography matrix of the reduced template image mapped to the reduced video image.
  • the position of the image block that is searched and matched with the reduced template image can be used as information to determine the rough positioning position of the reduced template image in the reduced video image; the determined reduced template image can also be mapped
  • the homography matrix in the reduced video image is used as information for determining a rough positioning position of the reduced template image in the reduced video image.
  • the reduced template image is already very small, in this embodiment, a small number of feature points are extracted from the reduced template image (for example, 4 feature points are extracted), and then the reduced template image is directly used. As the image blocks to be matched, the position of the image block matching the reduced template image is searched in the reduced video image, that is, the position of the reduced template image in the reduced video image.
  • Step 106 Determine the precise positioning position of the target in the video image according to the information of the rough positioning position.
  • the position of the reduced template image in the reduced video image is used as an example of the rough positioning position information.
  • the initial homography matrix of the reduced template image mapped to the reduced video image can be determined, and the initial homography matrix is mapped as the full-resolution template image to the homography matrix of the full-resolution video image.
  • the initial position of the full-resolution template image in the full-resolution video image is calculated, and according to the initial position of the template image in the full-resolution video image, the full-resolution The template image is matched with the full-resolution video image to determine the position of the full-resolution template image in the full-resolution video image, and this position is used as the precise positioning position of the target in the video image.
  • the matching process is the same as The matching process in step 104 is substantially the same, and is not repeated here.
  • Step 107 Output the precise positioning position.
  • the terminal may superimpose other images on the target in the video image according to the precise positioning position of the output target on the video image of the current frame.
  • the precise positioning position of the target on the video image of the current frame and the precise positioning position of the target on the video image of the historical frame the predicted position of the target in the next frame of video image is predicted.
  • the prediction method is here I will not repeat them here.
  • the target template image and the obtained video image are reduced, and the resolution of the template image and the video image is greatly reduced, so that the template image and the video image are being processed.
  • it can quickly determine the coarse positioning position information of the reduced template image in the reduced video image; the coarse positioning position information makes the process of determining the precise positioning position of the target in the video image reduced.
  • Searching the scope of the template image in the video image greatly improves the speed of determining the precise positioning position and shortens the time to determine the precise positioning position of the target in the video image; and in the case of an error in the predicted position, due to the reduced template
  • the low resolution and small image size of images and reduced video images make it possible to quickly determine the information of the rough positioning position without changing the scope of the search template image, so as not to lose the target template image, which improves the Accuracy of target tracking to improve user experience on AR .
  • the second embodiment of the present application relates to a target tracking method.
  • the second embodiment is a further improvement on the first embodiment.
  • the main improvement lies in that in this embodiment, the target is determined in the video image according to the information of the rough positioning position. Pyramid matching is used in the process of accurate positioning. The specific flow of this process is shown in Figure 2.
  • Step 201 Determine an initial homography matrix that maps the reduced template image to the reduced video image according to the information of the coarse positioning position.
  • this step 201 is substantially the same as the process of determining the homography matrix in step 106 in the first embodiment, that is, by determining the position information of the coarse positioning and the position of the reduced template image, it can be determined that the reduced template image is mapped to The initial homography matrix of the reduced video image.
  • Step 202 Determine the N-level resolution required in the pyramid matching process according to the initial homography matrix, where N is an integer greater than 1.
  • the use context of shooting video images is determined according to the initial homography matrix.
  • the use context includes: the shooting angle and the shooting distance; according to the use context, the N-level resolution required in the pyramid matching process is determined.
  • the posture information of the reduced template image in the reduced video image can be obtained according to the initial homography matrix, and the position of the reduced template image in the reduced video image can be obtained based on the coarse positioning position information.
  • the position in the reduced video image and the posture of the reduced template image in the reduced video image are used to determine the use situation of the captured video image. According to the use situation, the N-level resolution required in the pyramid matching process is determined.
  • the use situation is large-angle shooting, then determine two levels of resolution, the first level resolution is 1/2 resolution, and the second level resolution is full resolution; if the use situation is close range shooting, then Determine three levels of resolution, the first level of resolution is 1/4 resolution, the second level of resolution is 1/2 resolution, and the third level of resolution is full resolution.
  • Step 203 Perform pyramid matching on the video image according to the initial homography matrix and the determined N-level resolution, and determine the precise positioning position of the target in the video image according to the result of the pyramid matching.
  • the video image is scaled according to the N-level resolution to obtain the N-level video image corresponding to the N-level resolution, and the video images at each level are selected and obtained from the pre-stored template images of different resolutions.
  • Corresponding N-level template image the following processing is performed in order of resolution from low to high: according to the initial position corresponding to the i + 1-level resolution, the i + 1-level template image and the i + 1-level video image are processed Match to determine the homography matrix of the i + 1 level template image mapped to the i + 1 level video image, where the first level resolution is the lowest resolution among the N level resolutions and the initial corresponding to the first level resolution
  • the position is determined according to the initial homography matrix, and the initial position corresponding to the i + 1th level resolution is determined according to the homography matrix corresponding to the ith level resolution, N> 1, 1 ⁇ i ⁇ N-1; resolution according to the Nth level
  • the homography matrix corresponding to the rate determines the precise location of the target in the video image.
  • the video image can be pyramid-matched according to the initial homography matrix.
  • a specific example will be used to illustrate the process of pyramid matching.
  • the scene is used for large-angle shooting, determine that the first-level resolution is 1/2 resolution and the second-level resolution is full resolution.
  • the video image is scaled according to the first-level resolution to obtain a 1 / 2-resolution video image.
  • template images with different levels of resolution are stored in advance.
  • the pixel area S1 of the template image in the 1 / 2-resolution video image is calculated.
  • the template image 1 closest to the pixel area S1 is selected from the template images, and the template image 1 is used as the 1 / 2-resolution template image corresponding to the 1 / 2-resolution video image.
  • the second-level resolution is full resolution Rate, the video image does not need to be scaled, and the full-resolution template image is directly selected as the template image corresponding to the full-resolution video image.
  • Process in order of resolution from low to high According to the initial homography matrix H0, map a 1 / 2-resolution template image to a 1 / 2-resolution video image, and obtain a 1 / 2-resolution template image in The initial position in the 1 / 2-resolution video image. Based on the initial position corresponding to the first-level resolution, the first-level template image is matched with the first-level video image to determine that the first-level template image is mapped to the first level.
  • the matching process of the homography matrix H1 of the video image is substantially the same as the matching process in the first embodiment, which will not be repeated here.
  • the Nth level resolution is the full resolution of the video image; if not, according to the homography matrix corresponding to the Nth level resolution
  • the full-resolution template image is matched with the full-resolution video image to obtain the homography matrix corresponding to the full-resolution, and the full-resolution template image is determined based on the full-resolution video in the full-resolution video.
  • the positioning position in the image is used as the precise positioning position of the target in the video image; if it is, the positioning position of the full-resolution template image in the full-resolution video image is determined according to the homography matrix corresponding to the Nth level resolution And as the precise location of the target in the video image.
  • the N-th level resolution is not full resolution in the pyramid matching process, it is necessary to determine the positioning position of the full resolution template image in the full resolution video image.
  • the second-level resolution determines whether the second-level resolution is full resolution. Because it is determined that the second-level resolution is full resolution, you can directly convert the full resolution The positioning position of the resolution template image in the full-resolution video image is used as the precise positioning position of the target in the video image.
  • the process of matching the i + 1th level template image with the i + 1th level video image is: searching for the image block matching the i + 1th level template image in the i + 1th level video image, A homography matrix that maps the i + 1th-level template image to the i + 1th-level video image is determined according to the image block, and during the matching process, some pixels in the image block are used for matching.
  • the process of searching for an image block matching the i + 1 level template image in the i + 1 level video image, and finding a certain number of stable feature points in the i + 1 level template image (such as using Harris Corners to find feature points)
  • the matched image blocks can use a preset shape, Such as circles, rectangles, etc.
  • some pixels in the image block are used for matching. Among them, some of the pixels in the image block are distributed in a m-shaped or X-shape, and are distributed through the m-shaped or X-shaped pixels.
  • the target tracking method provided in this embodiment adopts a pyramid matching method in the process of determining the precise positioning position of a target in a video image, because the pyramid matching method is a hierarchical matching method , First match the low-resolution video image, then match the high-resolution video image, and constantly update the homography matrix that the target maps to the video image, so that the target can be accurately determined in the video image.
  • the full-resolution video image is not directly searched for the full-resolution template image, it starts from the low resolution and is based on the homography matrix obtained from the low-resolution video matching, which can be quickly determined.
  • the initial position of the full-resolution template image in the full-resolution video image is obtained, thereby quickly determining the precise positioning position of the target in the video image.
  • the third embodiment of the present application relates to a target tracking method.
  • the third embodiment is a further improvement of the second embodiment.
  • the main improvement is that if the precise positioning position is a pixel-level coordinate position in this embodiment,
  • the homography matrix corresponding to the N-level resolution determines the precise positioning position of the target in the video image, and then obtains the precise positioning position at the sub-pixel level.
  • the specific process of obtaining the accurate positioning position at the sub-pixel level is shown in Figure 3:
  • Step 301 Obtain a matching degree value of a precise positioning position, where the matching degree value is a similarity value matching a feature point of a target and a feature point of an image block in a video image.
  • the precise location of the determined target in the video image is also the pixel-level coordinate position.
  • the feature points of the target and the characteristics of the image block in the video image may be a sub-pixel. Therefore, in order to improve the accuracy of the precise positioning position of the target in the video image, the pixel level is processed.
  • the matching degree value of the precise location can be obtained during the matching process.
  • Step 302 Perform sub-pixel processing on the precisely-positioned position according to the matching degree value to obtain a precisely-positioned position at the sub-pixel level.
  • the coordinates corresponding to the feature points with the highest matching value are selected;
  • a two-dimensional Gaussian surface is constructed according to the pixel-level coordinates corresponding to the feature points with the highest matching degree value, for example, taking points around the highest matching point value to construct a two-dimensional Gaussian surface.
  • sub-pixel level accurate positioning can be determined.
  • point A is pixel-level coordinates A (3, 5)
  • the method provided in this embodiment obtains a sub-pixel-level precise positioning position by performing sub-pixel processing on the precise positioning position, which improves the accuracy of the precise positioning position and further improves the accuracy of the determined precise positioning position. degree.
  • the fourth embodiment of the present application relates to a target tracking device 40, including: a first acquisition module 401, a second acquisition module 402, an image reduction module 403, a predicted position reduction module 404, a coarse positioning module 405, and an accurate positioning module 406,
  • a target tracking device 40 including: a first acquisition module 401, a second acquisition module 402, an image reduction module 403, a predicted position reduction module 404, a coarse positioning module 405, and an accurate positioning module 406,
  • a target tracking device 40 including: a first acquisition module 401, a second acquisition module 402, an image reduction module 403, a predicted position reduction module 404, a coarse positioning module 405, and an accurate positioning module 406,
  • the specific structure is shown in Figure 4.
  • the first acquisition module 401 is used to acquire a frame of video image and the predicted position of the target in the video image; the second acquisition module 402 is used to acquire a reduced template image of the target; the image reduction module 403 is based on the reduced template image and Prediction position, determine the reduction ratio of the video image, and reduce the video image according to the reduction ratio to obtain a reduced video image; the prediction position reduction module 404 is used to determine the predicted position of the target in the reduced video image according to the predicted position; the coarse positioning module 405 is used to match the reduced template image with the reduced video image according to the predicted position of the target in the reduced video image to determine the rough positioning information of the reduced template image in the reduced video image; precise positioning The module 406 is configured to determine the precise positioning position of the target in the video image according to the rough positioning position information.
  • This embodiment is an embodiment of a virtual device corresponding to the foregoing method.
  • the technical details in the foregoing method embodiment are still applicable in this embodiment, and details are not described herein again.
  • a fifth embodiment of the present application relates to an electronic device 50, and its structure is shown in FIG. It includes: at least one processor 501; and a memory 502 communicatively connected to the at least one processor 501.
  • the memory 502 stores instructions executable by at least one processor 501.
  • the instructions are executed by at least one processor 501 to enable the at least one processor 501 to execute the above-mentioned target tracking method.
  • the memory 502 and the processor 501 are connected in a bus manner.
  • the bus may include any number of interconnected buses and bridges.
  • the bus links one or more processors 501 and various circuits of the memory 502 together.
  • the bus can also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art, so they are not described further herein.
  • the bus interface provides an interface between the bus and the transceiver.
  • a transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium.
  • the data processed by the processor 501 is transmitted on a wireless medium through an antenna. Further, the antenna also receives the data and transmits the data to the processor 501.
  • the processor 501 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions.
  • the memory 502 may be used to store data used by the processor when performing operations.
  • processor in this embodiment can execute the implementation steps in the foregoing method embodiments, and the specific execution functions are not described in detail. For technical details in the method embodiments, details are not described herein again.
  • the sixth embodiment of the present application relates to a computer-readable storage medium.
  • the readable storage medium is a computer-readable storage medium.
  • the computer-readable storage medium stores computer instructions that enable a computer to execute the first application of the present application.
  • the display method in the above embodiments is implemented by a program instructing related hardware.
  • the program is stored in a storage medium and includes several instructions to make a device (may It is a single-chip microcomputer, a chip, or the like) or a processor that executes all or part of the steps of the method described in each embodiment of the present application.
  • the aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), and a random access memory (RAM, Random-Access Memory), magnetic disks, or compact discs, which can store program code.

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Abstract

La présente invention appartient au domaine de la vision artificielle, et concerne en particulier un procédé et un appareil de suivi de cible, un dispositif électronique et un support de stockage lisible. Le procédé de suivi de cible comprend : l'acquisition d'une trame d'une image vidéo, et l'acquisition d'une position prédite d'une cible dans l'image vidéo ; la détermination d'une proportion de réduction d'échelle de l'image vidéo selon une image modèle réduite à l'échelle et la position prédite, et la réduction à l'échelle de l'image vidéo en fonction de la proportion de réduction d'échelle pour obtenir une image vidéo réduite à l'échelle ; selon la position prédite, la détermination d'une position prédite de la cible dans l'image vidéo réduite à l'échelle ; en fonction de la position prédite de la cible dans l'image vidéo réduite à l'échelle, la mise en correspondance de l'image modèle réduite à l'échelle avec l'image vidéo réduite à l'échelle pour déterminer des informations d'une position approximativement localisée de l'image modèle réduite à l'échelle dans l'image vidéo réduite à l'échelle ; et en fonction des informations de la position approximativement localisée, la détermination d'une position localisée avec précision de la cible dans l'image vidéo. Le procédé peut réaliser rapidement et avec précision la localisation de la position d'une cible dans une image vidéo, améliorant ainsi un effet d'expérience de réalité augmentée pour un utilisateur.
PCT/CN2018/096161 2018-07-18 2018-07-18 Procédé et appareil de suivi de cible, dispositif électronique et support de stockage lisible WO2020014901A1 (fr)

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