Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion

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	Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion
	Zhou, T. X.; M. Zhu; D. Zeng and H. Yang
刊名	Mathematical Problems in Engineering
	2017
英文摘要	Visual tracking is one of the most important components in numerous applications of computer vision. Although correlation filter based trackers gained popularity due to their efficiency, there is a need to improve the overall tracking capability. In this paper, a tracking algorithm based on the kernelized correlation filter (KCF) is proposed. First, fused features including HOG, color-naming, and HSV are employed to boost the tracking performance. Second, to tackle the fixed template size, a scale adaptive scheme is proposed which strengthens the tracking precision. Third, an adaptive learning rate and an occlusion detection mechanism are presented to update the target appearance model in presence of occlusion problem. Extensive evaluation on the OTB-2013 dataset demonstrates that the proposed tracker outperforms the state-of-the-art trackers significantly. The results show that our tracker gets a 14.79% improvement in success rate and a 7.43% improvement in precision rate compared to the original KCF tracker, and our tracker is robust to illumination variations, scale variations, occlusion, and other complex scenes.
语种	英语
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/59500]
专题	长春光学精密机械与物理研究所_中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Zhou, T. X.,M. Zhu,D. Zeng and H. Yang. Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion[J]. Mathematical Problems in Engineering,2017.
APA	Zhou, T. X.,M. Zhu,&D. Zeng and H. Yang.(2017).Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion.Mathematical Problems in Engineering.
MLA	Zhou, T. X.,et al."Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion".Mathematical Problems in Engineering (2017).