Adaptive target tracking method based on PSPCE

Abstract

The invention relates to an adaptive target tracking method based on PSPCE. A target to be tracked is determined in a tracking sequence, the current frame of the target is the tth frame, and the method comprises the following steps: S1, defining a search box according to the area position of the target in the previous frame, and calculating the feature map of an image block in the search box; s2,calculating the response of the filter and the feature map, and determining the target position of the current frame; s3, updating a PSPCE confidence sample filter queue according to the PSPCE coefficient to obtain a PSPCE confidence sample of the current filter; s4, adaptively updating the parameters of the current filter based on PSPCE regular constraints; and S5, repeating the steps S1 to S4 frame by frame until tracking of the tracking sequence is completed. According to the method, the correctness of the adaptive filter in the updating process can be ensured, the high-confidence filter can be selected on the time axis to update the PSPCE confidence sample, the situation that the filter suddenly changes under various changes of the appearance of the target is effectively prevented, andthe accuracy of target tracking is ensured.

Description

technical field [0001] The invention relates to a method for tracking a specified target in a video, specifically an adaptive target tracking method based on PSPCE. Background technique [0002] Target tracking is an important research problem in the field of computer vision, widely used in robotics, security monitoring, visual navigation, precision guidance and other fields. To imitate human visual perception, computers must be able to achieve speed and accuracy similar to human visual perception. In recent years, scholars have proposed a lot of excellent target tracking algorithms to address the challenges in the field of target tracking, such as occlusion, illumination changes, fast motion, motion blur, rotation and deformation, making target tracking technology achieve significant progress in recent years. Breakthrough and progress. However, it still cannot meet the visual perception ability approaching human beings in terms of accuracy and speed at the same time, and ...

AI Technical Summary

Problems solved by technology

[0004] These methods can improve considerable tracking speed and tracking accuracy on the original filter, but most of these methods use real-time update filters. When occlusion, background clutter, rotation and target deformation occur, the error template will be updated. The process is amplified, which eventually leads to model drift and tracking failure

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