Cyclic matrix video tracking method with partition

Abstract

The invention discloses a cyclic matrix video tracking method with partition. The method comprises a step of dividing a target into a plurality of sub targets with the same size and carrying out cosine window adding preprocessing on each sub target, a step of carrying out cyclic matrix tracking on each sub target respectively to obtain the tracking result of each sub target, and calculating the confidence level of the result, and a step of combining sub target tracking results and confidence levels to obtain the real position of the target. According to the method, the idea of partition is used, the target is subjected to partition processing, each sub target is subjected to cyclic matrix tracking respectively, the spatial distribution information of the target and a background is fully utilized, and the robustness of a video tracking algorithm is raised. According to the method, a complex scene video can be subjected to robust tracking, and the conditions of target scale change, serious blocking, target rapid change, 3D rotation and the like are effectively processed. In addition, the tracking speed is fast, the video processing speed can reach dozens of frames per second, and a real-time requirement is completely satisfied.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, and relates to a method for tracking a video target. Background technique [0002] Object tracking is an extremely challenging research topic in the field of computer vision, and has broad application prospects in intelligent video surveillance, augmented reality, gesture recognition, and autonomous driving. An efficient target tracking algorithm needs to solve the problems of target scale change, illumination change, partial occlusion, camera rotation and rapid target change during the tracking process. In the past two decades, many algorithms have been proposed, among which, the method based on online learning is the target Mainstream methods in the field of video tracking. [0003] Algorithms based on online learning can be mainly divided into two categories: target tracking algorithms based on generative models and target tracking algorithms based on discriminative mode...

AI Technical Summary

Problems solved by technology

However, the circulant matrix tracking algorithm is prone to lose the target when the target changes rapidly, scale changes and severe occlusions occur at the same time.

Because the circular matrix tracking algorithm only extracts the background information of the target itself and its surroundings, and ignores most of the other background information in the image, when the tracking scene is very complicated, the tracking will drift or even fail.

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