Robust visual tracking via augmented kernel SVM

	Robust visual tracking via augmented kernel SVM
	Bai, Yancheng; Tang, Ming
发表期刊	IMAGE AND VISION COMPUTING
	2014-08-01
卷号	32 期号:8 页码:465-475
文章类型	Article
摘要	Most current tracking approaches utilize only one type of feature to represent the target and learn the appearance model of the target just by using the current frame or a few recent ones. The limited representation of one single type of feature might not represent the target well. What's more, the appearance model learning from the current frame or a few recent ones is intolerant of abrupt appearance changes in short time intervals. These two factors might cause the track's failure. To overcome these two limitations, in this paper, we apply the Augmented Kernel Matrix (AKM) classification to combine two complementary features, pixel intensity and LBP (Local Binary Pattern) features, to enrich the target's representation. Meanwhile, we employ the AKM clustering to group the tracking results into a few aspects. And then, the representative patches are selected and added into the training set to learn the appearance model. This makes the appearance model cover more aspects of the target appearance and more robust to abrupt appearance changes. Experiments compared with several state-of-the-art methods on challenging sequences demonstrate the effectiveness and robustness of the proposed algorithm. (C) 2014 Elsevier B.V. All rights reserved.
关键词	Feature Representation Appearance Model Augmented Kernel Matrix (Akm)
WOS标题词	Science & Technology ; Technology ; Physical Sciences
关键词[WOS]	OBJECT TRACKING
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Optics
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Optics
WOS记录号	WOS:000339039600002
引用统计	被引频次：8[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/173211/2982
专题	多模态人工智能系统全国重点实验室_机器人视觉
作者单位	Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Bai, Yancheng,Tang, Ming. Robust visual tracking via augmented kernel SVM[J]. IMAGE AND VISION COMPUTING,2014,32(8):465-475.
APA	Bai, Yancheng,&Tang, Ming.(2014).Robust visual tracking via augmented kernel SVM.IMAGE AND VISION COMPUTING,32(8),465-475.
MLA	Bai, Yancheng,et al."Robust visual tracking via augmented kernel SVM".IMAGE AND VISION COMPUTING 32.8(2014):465-475.