Robust Visual Correlation Tracking

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	Robust Visual Correlation Tracking
	Zhang, L.; Y. J. Wang; H. H. Sun; Z. J. Yao and S. W. He
刊名	Mathematical Problems in Engineering
	2015
页码	13
英文摘要	Recent years have seen greater interests in the tracking-by-detection methods in the visual object tracking, because of their excellent tracking performance. But most existing methods fix the scale which makes the trackers unreliable to handle large scale variations in complex scenes. In this paper, we decompose the tracking into target translation and scale prediction. We adopt a scale estimation approach based on the tracking-by-detection framework, develop a new model update scheme, and present a robust correlation tracking algorithm with discriminative correlation filters. The approach works by learning the translation and scale correlation filters. We obtain the target translation and scale by finding the maximum output response of the learned correlation filters and then online update the target models. Extensive experiments results on 12 challenging benchmark sequences show that the proposed tracking approach reduces the average center location error (CLE) by 6.8 pixels, significantly improves the performance by 17.5% in the average success rate (SR) and by 5.4% in the average distance precision (DP) compared to the second best one of the other five excellent existing tracking algorithms, and is robust to appearance variations introduced by scale variations, pose variations, illumination changes, partial occlusion, fast motion, rotation, and background clutter.
收录类别	SCI ; EI
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/55167]
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	Zhang, L.,Y. J. Wang,H. H. Sun,et al. Robust Visual Correlation Tracking[J]. Mathematical Problems in Engineering,2015:13.
APA	Zhang, L.,Y. J. Wang,H. H. Sun,&Z. J. Yao and S. W. He.(2015).Robust Visual Correlation Tracking.Mathematical Problems in Engineering,13.
MLA	Zhang, L.,et al."Robust Visual Correlation Tracking".Mathematical Problems in Engineering (2015):13.