Online Multi-target Tracking Method Based on Trajectory Metric Learning

A multi-target tracking and metric learning technology, which is applied in the field of online multi-target tracking, can solve the problems of poor practicability and achieve the effects of good practicability, improved tracking effect, and enhanced resolution ability

Active Publication Date: 2022-05-31
NORTHWESTERN POLYTECHNICAL UNIV
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AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of poor practicability of existing online multi-target tracking methods, the present invention provides an online multi-target tracking method based on trajectory metric learning

Method used

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  • Online Multi-target Tracking Method Based on Trajectory Metric Learning
  • Online Multi-target Tracking Method Based on Trajectory Metric Learning
  • Online Multi-target Tracking Method Based on Trajectory Metric Learning

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Embodiment Construction

[0051] refer to Figure 1-3 . The specific steps of the online multi-target tracking method based on trajectory metric learning of the present invention are as follows:

[0052] Step 1. Consider the length of the trajectory, the degree of occlusion of the target, the closeness of the trajectory and the detection response, the smoothness of the trajectory and other factors, and design the trajectory confidence function:

[0053]

[0054] in represents the trajectory length of the i-th target up to time t, represents the closeness between the i-th target and its corresponding detection response at time k, Indicates the degree to which the ith target at time k is not occluded, T k is all target trajectories at time k, smo(T t i ) represents the smoothness of the trajectory of the i-th target up to time t.

[0055] Step 2. For the trajectory T of the i-th target at time t t i , use the Kalman filter to estimate the position of the target at time k Considering the ...

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Abstract

The invention discloses an online multi-target tracking method based on trajectory metric learning, which is used to solve the technical problem of poor practicability of the existing online multi-target tracking method. The technical solution is to first use the existing target detection algorithm to generate a detection response; then, divide the existing trajectory set into a high confidence set and a low confidence set, and use static features and traditional measurement methods to process the high confidence set and the next moment detection response For the data connection problem, the similarity measure matrix is ​​used to enhance the data connection ability for low confidence sets, and the final result is obtained. The present invention uses the existing trajectory information as a training sample set to learn the similarity measurement matrix between the trajectory and the detection response online, thereby enhancing the discrimination ability of trajectory discrimination. It solves the technical problems that the background technology method is difficult to accurately connect data, and the tracking effect is severely limited by the detection effect, improves the multi-target tracking effect, and has good practicability.

Description

technical field [0001] The invention relates to an online multi-target tracking method, in particular to an online multi-target tracking method based on trajectory metric learning. Background technique [0002] Visual multi-target tracking can match the detection responses at different times one by one given the video sequence and target detection results, and finally obtain the dynamic changes of the target in the temporal and spatial domains. As an important part of visual perception and understanding, multi-target tracking can effectively extract the motion information and temporal change information of targets, and can assist smart devices to accurately and robustly understand the surrounding environment in both the temporal and spatial domains. For example, applying multi-target tracking to an intelligent monitoring system will help detect abnormal targets; applying it to smart cars will help avoid rear-end collisions, collisions and other traffic accidents; applying to...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/246G06V10/56G06V10/50
Inventor 王琦李学龙张星宇
Owner NORTHWESTERN POLYTECHNICAL UNIV
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