LAUSR

In the process of target tracking for UAV video images, the performance of the tracking algorithm declines or even the tracking fails due to target occlusion and scale variation. This paper proposes an improved target tracking algorithm based on the analysis of the tracking framework of the kernel correlation filter. First, four subblocks around the center of the target center are divided. A correlation filter fusing Histogram of Oriented Gradient (HOG) feature and Color Name (CN) feature tracks separately each target subblocks. According to the spatial structure characteristics in the subblocks, the center location and scale of the target are estimated. Secondly, the correct center location of target is determined by the global filter. Then, a tracking fault detection method is proposed. When tracking fails, the target redetection module which uses the normalized cross-correlation algorithm (NCC) to obtain the candidate target set in the re-detection area is started. Besides, this algorithm uses the global filter to obtain real target from the candidate set. In the meanwhile, this algorithm adjusts sectionally the learning rate of the classifiers according to detection results. Lastly, the performance of this algorithm is verified on the UAV123 dataset. The results show that compared with several mainstream methods, that of this algorithm is significantly improved when dealing with target scale variation and occlusion.