Target tracking method based on objectivity and correlation filter

Abstract

The invention discloses a target tracking method based on objectivity and a correlation filter, comprising: expanding a target region in a frame, constructing a training sample and training a kernelized correlation filter, calculating a forward optical flow of a search region in the current frame when each new frame of image comes, obtaining boundaries of a moving object through an edge detectionalgorithm in combination with the color information of the search region, generating a target candidate by an objectivity method according to an edge map generated by edge detection, and adjusting thefour boundaries of a candidate window to fit the true edges of the object. In the search region of the current frame, the initial target position corresponding to the maximum response value is detected by the kernelized correlation filter, the candidate response value is obtained by calculating the objectivity, the target position and size of the current frame are obtained by comparing the response values and the model is updated. The method can effectively deal with the problems such as fast motion and scale change, thereby achieving robust tracking.

Description

technical field [0001] The present invention relates to the field of computer vision, more specifically, to a method for object tracking based on object-likeness and correlation filtering. Background technique [0002] As an important research direction in the field of computer vision, object tracking has received extensive attention. This technology has broad application prospects in the fields of security monitoring, unmanned driving and military defense. Although there are already a considerable number of object tracking methods, these methods are often unstable or even invalid under the conditions of illumination changes, object deformation, fast motion and severe occlusion. Therefore, proposing an effective target tracking algorithm has important application value and practical significance. [0003] In recent years, due to the simplicity and high efficiency of correlation filtering, the tracking algorithm based on correlation filtering has become the most common meth...

AI Technical Summary

Problems solved by technology

However, there is still a big difference between the image of the video sequence and the static image in the target tracking problem. First, the resolution is much lower than that of the static image. not widely used

At the same time, the timing information between adjacent frames plays an important role in the target tracking problem, which is not fully considered by using a single frame as a static image for object-likeness detection

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