Abstract
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.
Highlights
An unmanned aerial vehicle (UAV) is an aircraft without a human pilot on board, which exploits radio remote control equipment or self-provided program control devices
4.1 Experiment setup In order to comprehensively evaluate the effectiveness of the proposed algorithm in this paper, the proposed algorithm is compared with five correlation filtering algorithms with excellent comprehensive performance on the UAV123 benchmark dataset [29]
This paper studies the problem of target tracking for UAV video images
Summary
An unmanned aerial vehicle (UAV) is an aircraft without a human pilot on board, which exploits radio remote control equipment or self-provided program control devices. The tracking algorithm based on the correlation filter can well adapt to the variation of the target appearance, considering its extremely fast computation speed and good positioning performance in the Fourier domain. For the scale evaluation problem, the DSST tracker in [18] exploits the HOG feature to learn an adaptive multi-scale correlation filter, which aims to evaluate the scale variation of the object target. How to devise a more steady and accurate tracking algorithm is a challenging problem in UAV target tracking. This paper proposes an improved KCF algorithm in combination with abilities based on parts and redetection. In an attempt to improve the tracking performance of the algorithm under occlusion, part-based tracking strategy fused with the multi-feature is employed on the basis of the traditional KCF algorithm.
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