关键词: KCF算法, Harris角点检测, 匈牙利算法, 广义霍夫算法

Abstract:

In general, Kernel Correlation Filter（KCF） algorithm is vulnerable to actual detection including occlusion and other conditions. In order to make the tracking result more accurate, an optimized KCF approach combined with improved corner detection is proposed in this paper. With the appropriate number and strong robustness of adaptive Harris corner points, problems of slow extraction of redundant edge points and incomplete edge points caused by illumination variation in generalized Hough algorithm are solved. At the same time, the introduction of adaptive threshold approach minimizes the influence of noise on corner extraction. Subsequently, targets are segmented and tracked respectively. The problem that KCF algorithm is easy to lose targets when they have scale variation is solved according to relative positions of sub-blocks. The learning rate parameter is updated adaptively to reduce learning rate of KCF algorithm and the error of model updating when the target is occluded. Finally, to eliminate the drift phenomenon of KCF when the target moves rapidly, intersection over union and Hungarian algorithm are combined to correlate multiple targets, associated coordinates are taken out one by one and the final position is found using the outline of the target drawn by generalized Hough algorithm. Experiments show that the approach can improve the reliability of target tracking effectively.

Key words: Kernel Correlation Filter（KCF） algorithm, Harris corner detection, Hungarian algorithm, generalized Hough algorithm