Scale adaptive target tracking method based on fast compressive tracking algorithm

Abstract

The invention discloses a scale adaptive target tracking method based on a fast compressive tracking algorithm. Firstly, a context model is used to weight a quasi-Haar feature, and the robustness of the quasi-Haar feature towards illumination changes is enhanced; then, stepped tracking is adopted, the anti-blocking ability of the algorithm and the ability of the algorithm handing the target scalechanges are enhanced, and the real-time performance of the algorithm is also kept; and finally, a scale adaptive method is put forward, and stable tracking on the scale change target is realized. Goodrobustness is achieved in conditions of target scale change, target appearance change and target being blocked, the frame frequency can be ensured to be about 39 frames per second, and requirements of real-time performance are met.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to video target tracking, face recognition, online learning and scale self-adaptation, etc., in particular to a scale-adaptive target tracking method based on a fast compression tracking algorithm. Background technique [0002] Object tracking is a research hotspot in the field of computer vision, and it has a wide range of applications in motion analysis, behavior recognition, intelligent monitoring, human-computer interaction and other fields [1,2]. The difficulty of target tracking is how to deal with the changes in the appearance of the target itself and the influence of factors such as illumination, occlusion, and background changes on the target [3,4]. In recent years, the target tracking algorithm based on online learning has received extensive attention. This algorithm regards the tracking problem as a special binary classification problem. The key is to...

AI Technical Summary

Problems solved by technology

But it mainly has two problems: First, the feature description is simple, and it is prone to tracking drift or target loss when the illumination changes or the appearance of the target changes greatly.

Second, the scale of the target window is fixed during the tracking process. When the target scale becomes larger or occluded, it is easy to cause tracking drift or target loss.

Literature [11] is the original author's improvement of the CT algorithm, but it only improves the processing speed of the algorithm, and does not solve the above problems well

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