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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)
作者部门机器人学研究室
关键词Monte Carlo Methods Signal Filtering And Prediction
发表期刊Pattern Recognition
ISSN0031-3203
2014
卷号47期号:12页码:3828–3840
收录类别SCI ; EI
EI收录号20143600016923
WOS记录号WOS:000342870900008
产权排序2
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.
语种英语
WOS标题词Science & Technology ; Technology
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
关键词[WOS]VISUAL TRACKING ; MODEL
WOS研究方向Computer Science ; Engineering
引用统计
被引频次:10[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/15121
专题机器人学研究室
通讯作者Fan BJ(范保杰)
作者单位1.College of Automation, Nanjing University of Posts and Telecommunications, No.9 Wenyuan Road, Nanjing, China
2.State Key Laboratory of Robotics, Shenyang Institute Automation, Chinese Academy of Sciences, Shenyang, China
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.
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