An Approach to Overcome Occlusions in Visual Tracking By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training

doi:10.1109/lsp.2018.2856102

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	An Approach to Overcome Occlusions in Visual Tracking By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training
	Jiang, K. W.; Qian, F.; Song, C.; Zhang, B.
	2018
发表期刊	Ieee Signal Processing Letters
ISSN	1070-9908
卷号	25 期号:12 页码:1890-1894
摘要	Visual tracking methods have been successful in recent years. Correlation filter (CF) based methods significantly advanced state-of-the-art tracking. The advancement in CF tracking performance is predominantly attributed to powerful features and sophisticated online learning formulations. However, there would be trouble if the tracker indiscriminately learned samples. Particularly, when the target is severely occluded or out-of-view, the tracker will continuously learn the wrong information, resulting target loss in the following frames. In this study, aiming to avoid incorrect training when occlusions occur, we propose a regional color histogram-based occlusion estimating agency (RCHBOEA), which estimates the occlusion level and then instructs, based on the result, the tracker to work in one of two modes: normal or lost. In the normal mode, an occlusion level-based self-adopting learning rate is used for tracker training. In the lost mode, the tracker pauses its training and conducts a search and recapture strategy on a wider searching area. Our method can easily complement CF-based trackers. In our experiments, we employed four CF-based trackers as a baseline: discriminative CFs (DCF), kernelized CFs (KCF), background-aware CFs (BACF), and efficient convolution operators for tracking: hand-crafted feature version (ECO_HC). We performed extensive experiments on the standard benchmarks: VIVID, OTB50, and OTB100. The results demonstrated that combined with RCHBOEA, the trackers achieved a remarkable improvement.
关键词	Overcome occlusion regional color histogram (RCH) self-adopting learning rate visual tracking object tracking benchmark Engineering
DOI	10.1109/lsp.2018.2856102
收录类别	SCI ; EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/61223
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Jiang, K. W.,Qian, F.,Song, C.,et al. An Approach to Overcome Occlusions in Visual Tracking By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training[J]. Ieee Signal Processing Letters,2018,25(12):1890-1894.
APA	Jiang, K. W.,Qian, F.,Song, C.,&Zhang, B..(2018).An Approach to Overcome Occlusions in Visual Tracking By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training.Ieee Signal Processing Letters,25(12),1890-1894.
MLA	Jiang, K. W.,et al."An Approach to Overcome Occlusions in Visual Tracking By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training".Ieee Signal Processing Letters 25.12(2018):1890-1894.

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