Robust Online Learned Spatio-Temporal Context Model for Visual Tracking

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Robust Online Learned Spatio-Temporal Context Model for Visual Tracking
	Wen, Longyin 1,2; Cai, Zhaowei 1,2; Lei, Zhen1,2 ; Yi, Dong 1,2; Li, Stan Z.1,2
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2014-02-01
卷号	23 期号:2 页码:785-796
文章类型	Article
摘要	Visual tracking is an important but challenging problem in the computer vision field. In the real world, the appearances of the target and its surroundings change continuously over space and time, which provides effective information to track the target robustly. However, enough attention has not been paid to the spatio-temporal appearance information in previous works. In this paper, a robust spatio-temporal context model based tracker is presented to complete the tracking task in unconstrained environments. The tracker is constructed with temporal and spatial appearance context models. The temporal appearance context model captures the historical appearance of the target to prevent the tracker from drifting to the background in a long-term tracking. The spatial appearance context model integrates contributors to build a supporting field. The contributors are the patches with the same size of the target at the key-points automatically discovered around the target. The constructed supporting field provides much more information than the appearance of the target itself, and thus, ensures the robustness of the tracker in complex environments. Extensive experiments on various challenging databases validate the superiority of our tracker over other state-of-the-art trackers.
关键词	Visual Tracking Spatio-temporal Context Multiple Subspaces Learning Online Boosting
WOS标题词	Science & Technology ; Technology
关键词[WOS]	OBJECTS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000329581800023
引用统计	被引频次：46[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8032
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
作者单位	1.Chinese Acad Sci, Inst Automat, Ctr Biometr & Secur Res, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所; 模式识别国家重点实验室
推荐引用方式 GB/T 7714	Wen, Longyin,Cai, Zhaowei,Lei, Zhen,et al. Robust Online Learned Spatio-Temporal Context Model for Visual Tracking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(2):785-796.
APA	Wen, Longyin,Cai, Zhaowei,Lei, Zhen,Yi, Dong,&Li, Stan Z..(2014).Robust Online Learned Spatio-Temporal Context Model for Visual Tracking.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(2),785-796.
MLA	Wen, Longyin,et al."Robust Online Learned Spatio-Temporal Context Model for Visual Tracking".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.2(2014):785-796.