Discriminative multi-task objects tracking with active feature selection and drift correction

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	Discriminative multi-task objects tracking with active feature selection and drift correction
	Fan BJ(范保杰); Cong Y(丛杨); Du YK(杜英魁)
刊名	Pattern Recognition
	2014
卷号	47 期号:12 页码:3828–3840
关键词	Monte Carlo methods Signal filtering and prediction
ISSN号	0031-3203
通讯作者	范保杰
产权排序	2
中文摘要	In this paper, we propose a discriminative multi-task objects tracking method with active feature selection and drift correction. The developed method formulates object tracking in a particle filter framework as multi-Task discriminative tracking. As opposed to generative methods that handle particles separately, the proposed method learns the representation of all the particles jointly and the corresponding coefficients are similar. The tracking algorithm starts from the active feature selection scheme, which adaptively chooses suitable number of discriminative features from the tracked target and background in the dynamic environment. Based on the selected feature space, the discriminative dictionary is constructed and updated dynamically. Only a few of them are used to represent all the particles at each frame. In other words, all the particles share the same dictionary templates and their representations are obtained jointly by discriminative multi-task learning. The particle that has the highest similarity with the dictionary templates is selected as the next tracked target state. This jointly sparsity and discriminative learning can exploit the relationship between particles and improve tracking performance. To alleviate the visual drift problem encountered in object tracking, a two-stage particle filtering algorithm is proposed to complete drift correction and exploit both the ground truth information of the first frame and observations obtained online from the current frame. Experimental evaluations on challenging sequences demonstrate the effectiveness, accuracy and robustness of the proposed tracker in comparison with state-of-the-art algorithms. © 2014 Elsevier Ltd.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
关键词[WOS]	VISUAL TRACKING ; MODEL
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000342870900008
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/15121]
专题	沈阳自动化研究所_机器人学研究室
推荐引用方式 GB/T 7714	Fan BJ,Cong Y,Du YK. Discriminative multi-task objects tracking with active feature selection and drift correction[J]. Pattern Recognition,2014,47(12):3828–3840.
APA	Fan BJ,Cong Y,&Du YK.(2014).Discriminative multi-task objects tracking with active feature selection and drift correction.Pattern Recognition,47(12),3828–3840.
MLA	Fan BJ,et al."Discriminative multi-task objects tracking with active feature selection and drift correction".Pattern Recognition 47.12(2014):3828–3840.