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CN-116012421-B - Target tracking method and device

CN116012421BCN 116012421 BCN116012421 BCN 116012421BCN-116012421-B

Abstract

The invention provides a multi-target tracking method and device, the method comprises the steps of carrying out target detection on a target tracking image acquired in advance to obtain a target detection result corresponding to the target tracking image, wherein the target detection result comprises a plurality of target detection frames with different target categories; and carrying out target prediction on the currently tracked target tracking image by utilizing a multi-target tracker to obtain a target prediction result, wherein the target prediction result comprises a plurality of target prediction frames with different target categories, and carrying out association matching on the target prediction frames with different target categories and the target detection frames with different target categories so as to realize multi-target tracking. According to the invention, the target tracking images are respectively subjected to target detection and target prediction so as to uniformly correlate the detected target detection frames with the predicted target prediction frames with different types, so that the time consumption of a matching algorithm can be obviously reduced, and the time sequence correlation effect is not influenced.

Inventors

  • JIANG WEI

Assignees

  • 际络科技(上海)有限公司

Dates

Publication Date
20260508
Application Date
20221222

Claims (7)

  1. 1. A multi-target tracking method, comprising: Performing target detection on a target tracking image acquired in advance to obtain a target detection result corresponding to the target tracking image, wherein the target detection result comprises a plurality of target detection frames with different target categories; Performing target prediction on the currently tracked target tracking image by utilizing a multi-target tracker to obtain a target prediction result, wherein the target prediction result comprises a plurality of target prediction frames with different target categories; Performing association matching on the target prediction frames of a plurality of different target categories and the target detection frames of a plurality of different target categories so as to realize multi-target tracking; the performing association matching between the target prediction frames of the plurality of different target categories and the target detection frames of the plurality of different target categories includes: Obtaining corresponding measurement values according to the target prediction frames and the target detection frames; According to the measurement value and based on a preset association algorithm, matching a plurality of target detection frames of different target categories of the target tracking image with a plurality of target prediction frames of different target categories to obtain target matching results corresponding to the target categories; displaying the target tracking images as matching pairs based on the target matching results, wherein the continuous frame numbers of the target tracking images corresponding to the matching pairs accord with preset frame numbers, and obtaining target tracking results; After the target matching results corresponding to the target categories are obtained, the method comprises the following steps: Based on the target matching result, displaying the target matching result as a matching pair, comparing the measurement value with a preset threshold value, and judging whether the categories of the target prediction frame and the target detection frame are consistent; If the measurement value is larger than or equal to the preset threshold value and the categories are consistent, updating the multi-target tracker by using the target detection result, otherwise, removing the pairing relation of the corresponding pairing pairs, updating the target matching result of the corresponding target prediction frame into an unmatched prediction frame, and updating the target matching result of the corresponding target detection frame into an unmatched detection frame; the target detection result further includes detection grounding points corresponding to the target detection frames of the plurality of different target categories one by one, and the updating the multi-target tracker by using the target detection result includes: And updating the multi-target tracker by utilizing a target detection frame matched with the target prediction frame and a detection grounding point corresponding to the target detection frame, so as to perform forward Kalman filtering on the target prediction result by utilizing the updated multi-target tracker.
  2. 2. The object tracking method according to claim 1, further comprising, after the obtaining the object matching result corresponding to each object category: initializing the multi-target tracker based on the target matching result being displayed as a non-matching detection frame; and based on the target matching result displayed as a non-matching prediction frame, recording the corresponding continuous non-matching times.
  3. 3. The target tracking method according to claim 1, wherein when the target tracking image acquired in advance is a cloud offline video, after performing association matching on the target prediction frames of the plurality of different target categories and the target detection frames of the plurality of different target categories, the method further comprises: And based on the target matching result displayed as a matching pair, performing backward Kalman filtering on target prediction results of time sequence points forming the same track.
  4. 4. The object tracking method according to claim 3, wherein the performing backward kalman filtering on the object prediction results of each timing point constituting the same track based on the object matching result displayed as a matching pair, comprises: Based on the target matching result displayed as a matching pair, performing optimal state estimation on targets of time sequence points forming the same track to obtain a corresponding optimal estimation result; Based on each time sequence point and the optimal estimation result, a Gaussian distribution sequence is obtained; And reversely iterating the Gaussian distribution sequence and the target detection frames of the time sequence points obtained in advance into a backward Kalman filter to carry out track smoothing.
  5. 5. An object tracking device, comprising: the target detection module is used for carrying out target detection on a target tracking image acquired in advance to obtain a target detection result corresponding to the target tracking image, wherein the target detection result comprises a plurality of target detection frames with different target categories; The target prediction module is used for carrying out target prediction on a currently tracked target tracking image by utilizing a multi-target tracker to obtain a target prediction result, wherein the target prediction result comprises a plurality of target prediction frames with different target categories; the tracking module is used for carrying out association matching on the target prediction frames of the plurality of different target categories and the target detection frames of the plurality of different target categories so as to realize multi-target tracking; the tracking module comprises: the measuring unit is used for obtaining corresponding measuring values according to the target prediction frames and the target detection frames; the matching unit is used for matching the target detection frames of the target tracking images in different target categories with the target prediction frames of the target tracking images in different target categories based on a preset association algorithm according to the measurement value to obtain target matching results corresponding to the target categories; The target tracking unit displays the target tracking result as a matching pair based on the target matching result, and the continuous frame number of the target tracking image corresponding to the matching pair accords with the preset frame number to obtain a target tracking result; The tracking module further comprises: the comparison judging unit is used for comparing the measurement value with a preset threshold value based on the target matching result displayed as a matching pair after the target matching result corresponding to each target category is obtained, and judging whether the categories of the target prediction frame and the target detection frame are consistent; The updating unit is used for updating the multi-target tracker by utilizing the target detection result if the measurement value is larger than or equal to the preset threshold value and the categories are consistent, otherwise, removing the pairing relation of the corresponding matching pairs, updating the target matching result of the corresponding target prediction frame into an unmatched prediction frame and updating the target matching result of the corresponding target detection frame into an unmatched detection frame; The object detection result further includes detection grounding points corresponding to the object detection frames of the plurality of different object categories one by one, and the updating unit includes: And the updating subunit is used for updating the multi-target tracker by utilizing a target detection frame matched with the target prediction frame and a detection grounding point corresponding to the target detection frame so as to perform forward Kalman filtering on the target prediction result by utilizing the updated multi-target tracker.
  6. 6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the object tracking method according to any one of claims 1 to 4 when the program is executed by the processor.
  7. 7. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the object tracking method according to any one of claims 1 to 4.

Description

Target tracking method and device Technical Field The present invention relates to the field of image processing technologies, and in particular, to a target tracking method and apparatus. Background With the wide application of image processing in civil and commercial fields, multi-target tracking plays an increasingly important role in the fields of intelligent video monitoring, automatic driving, unmanned supermarkets and the like, so that target tracking, particularly multi-target tracking, also faces higher requirements. In a monocular ADAS system, the accuracy and stability (especially stability in time sequence) of target tracking plays a critical role for upper ranging, speed measurement and alarm logic, while the attributes of target detection tracking include at least frame and ground point. The actual ADAS system is deployed and applied, not only has a vehicle end, but also can have a cloud end, particularly for the vehicle end with low calculation force, the video (such as 10 s) with the current extremely short time is uploaded to the cloud end, the calculation result can be obtained under the time consumption lower than the vehicle end by depending on the high calculation force of the cloud end, and the vehicle end is updated and corrected. However, there is currently little monocular ADAS research on low-end computing devices at the vehicle end, and multi-objective tracking research has focused mainly on online tracking under the tracking-by-detection framework, such as SORT, deepSort, etc. In addition, there is a recent trend of integration of detection and tracking, such as JDE, fairMoT, and the like. But these studies basically all belong to online single-category single-attribute multi-target tracking, such as the frame attribute of pedestrian category, the gesture attribute of human category, the frame attribute of vehicle category, etc. In addition, at present, tracking can be performed based on deep learning, such as detection, feature extraction, matching and the like, even detection and tracking are integrated end to end, but the deep learning relies on large-scale data, the video stream annotation required by tracking consumes far more frame annotation than target detection, the generalization capability of a scene which is not covered by training data is obviously weaker than SORT and the like, and the effect of filtering and smoothing is not provided with a time sequence. Disclosure of Invention The invention provides a target tracking method and device, which are used for solving the defect of poor accuracy and stability of multi-target tracking in the prior art and can greatly improve the accuracy and time sequence stability of target tracking. The invention provides a target tracking method, which comprises the steps of carrying out target detection on a target tracking image acquired in advance to obtain a target detection result corresponding to the target tracking image, carrying out target prediction on the target tracking image tracked currently by utilizing a multi-target tracker to obtain a target prediction result, wherein the target prediction result comprises a plurality of target prediction frames of different target categories, and carrying out association matching on the target prediction frames of different target categories and the target detection frames of different target categories so as to realize multi-target tracking. The target tracking method comprises the steps of carrying out association matching on target prediction frames of a plurality of different target categories and target detection frames of a plurality of different target categories, obtaining corresponding metric values according to the target prediction frames and the target detection frames, carrying out matching on the target detection frames of the plurality of different target categories and the target prediction frames of the plurality of different target categories of the target tracking image according to the metric values and based on a preset association algorithm, obtaining target matching results of the target categories, displaying the target matching results as matching pairs, and enabling the continuous frame numbers of the target tracking image corresponding to the matching pairs to meet the preset frame numbers to obtain the target tracking results. The target tracking method comprises the steps of displaying target matching results corresponding to all target categories as matching pairs, comparing the metric value with a preset threshold value, judging whether the categories of the target prediction frame and the target detection frame are consistent or not, updating the multi-target tracker by using the target detection results if the metric value is greater than or equal to the preset threshold value and the categories are consistent, otherwise, removing the matching relation of the corresponding matching pairs, updating the target matching results of the corresponding tar