A novel anti-drift visual object tracking algorithm based on sparse response and adaptive spatial-temporal context-aware

doi:10.3390/rs13224672

CIOMP OpenIR

	A novel anti-drift visual object tracking algorithm based on sparse response and adaptive spatial-temporal context-aware
	Y. Su; J. Liu; F. Xu; X. Zhang and Y. Zuo
	2021
发表期刊	Remote Sensing
ISSN	20724292
卷号	13 期号:22
摘要	Correlation filter (CF) based trackers have gained significant attention in the field of visual single-object tracking, owing to their favorable performance and high efficiency; however, existing trackers still suffer from model drift caused by boundary effects and filter degradation. In visual tracking, long-term occlusion and large appearance variations easily cause model degradation. To remedy these drawbacks, we propose a sparse adaptive spatial-temporal context-aware method that effectively avoids model drift. Specifically, a global context is explicitly incorporated into the correlation filter to mitigate boundary effects. Subsequently, an adaptive temporal regularization constraint is adopted in the filter training stage to avoid model degradation. Meanwhile, a sparse response constraint is introduced to reduce the risk of further model drift. Furthermore, we apply the alternating direction multiplier method (ADMM) to derive a closed-solution of the object function with a low computational cost. In addition, an updating scheme based on the APEC-pool and Peak-pool is proposed to reveal the tracking condition and ensure updates of the targets appearance model with high-confidence. The Kalam filter is adopted to track the target when the appearance model is persistently unreliable and abnormality occurs. Finally, extensive experimental results on OTB-2013, OTB-2015 and VOT2018 datasets show that our proposed tracker performs favorably against several state-of-the-art trackers. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
DOI	10.3390/rs13224672
URL	查看原文
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/181722/65465
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	Y. Su,J. Liu,F. Xu,et al. A novel anti-drift visual object tracking algorithm based on sparse response and adaptive spatial-temporal context-aware[J]. Remote Sensing,2021,13(22).
APA	Y. Su,J. Liu,F. Xu,&X. Zhang and Y. Zuo.(2021).A novel anti-drift visual object tracking algorithm based on sparse response and adaptive spatial-temporal context-aware.Remote Sensing,13(22).
MLA	Y. Su,et al."A novel anti-drift visual object tracking algorithm based on sparse response and adaptive spatial-temporal context-aware".Remote Sensing 13.22(2021).

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