A tracking algorithm based on high confidence update supplementary learning

A tracking algorithm and high-confidence technology, applied in the field of image processing, can solve problems such as not considering the reliability of the current frame result, tracking failure, etc., to achieve effective tracking and improve robustness

Inactive Publication Date: 2018-12-18
NANJING UNIV OF INFORMATION SCI & TECH
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AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved in the present invention is to solve the problem that the existing target tracking algorithm does not consider the reliability of the current frame result when up

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  • A tracking algorithm based on high confidence update supplementary learning
  • A tracking algorithm based on high confidence update supplementary learning
  • A tracking algorithm based on high confidence update supplementary learning

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Embodiment 1

[0041] Embodiment 1: as figure 1 , 2 As shown, the tracking algorithm (HCLT) based on high-confidence update supplementary learning includes training the ridge regression filter classifier that can detect the frame picture, inputting the current frame and classifier parameters to obtain the detection response value of the correlation filter classifier of the current frame, Calculate the confidence of the relevant filter response, according to the confidence s n with threshold θ n Determine whether to update the classifier parameters, calculate the number of consecutive non-updated frames and force updates for more than 10 frames, obtain the final tracking position by fusing the response of the color complement learner, and output the current frame tracking results and classifier parameters Seven steps; the specific process is as follows:

[0042] (1) Use the standard correlation filtering framework to train a ridge regression filter classifier that can detect each frame of ...

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Abstract

The invention discloses a tracking algorithm based on high confidence update supplementary learning, which belongs to the field of image processing. At first, the correlation filter response value isobtained according to the standard correlation filter classifier, and the peak-to-side ratio of the correlation filter response is calculated as a response confidence level; if the confidence level isgreater than an average threshold, the current frame continues to update the correlation filter, and if the confidence level is less than an average threshold, the update of the filter is stopped; then, the number of frames that are not updated continuously is calculated, and if there are 10 consecutive frames that are not updated, the frames are updated forcibly; finally, the total response is obtained by fusing the response of the color supplementary learner, and the position of the maximum value in the response is the tracking result. The invention remarkably improves the robustness of thetracking algorithm, can effectively distinguish the target from the background, further improves the precision of the tracker, and remarkably improves the robustness and accuracy of the tracker underthe condition that the illumination changes drastically and the target scene is complex. The algorithm is free of the influence of the change of the tracking target environment, and can effectively and accurately track the target object.

Description

technical field [0001] The invention relates to a tracking algorithm based on high-confidence update supplementary learning, especially a video tracking method, which belongs to the field of image processing. Background technique [0002] Object tracking is one of the core problems in the field of computer vision, and it has a wide range of applications in human-computer interaction, video surveillance, augmented reality and other fields. Although a lot of progress has been made in this field in the past few decades, tracking arbitrary objects is still a very challenging task due to disturbances such as illumination changes, geometric deformations, partial occlusions, background clutter, fast motion, etc. [0003] In recent years, many target tracking algorithms based on correlation filtering have emerged, which can perform better single target tracking in video, among which the real-time color supplementary learning target tracking algorithm is representative. However, the...

Claims

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Application Information

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IPC IPC(8): G06T7/20
CPCG06T7/20G06T2207/10016G06T2207/20024G06T2207/20056
Inventor 宋慧慧樊佳庆张开华范蓉蓉刘青山
Owner NANJING UNIV OF INFORMATION SCI & TECH
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