Discriminative multi-task objects tracking with active feature selection and drift correction
文献类型：期刊论文
| 作者 | Fan BJ(范保杰) ; Cong Y(丛杨); Du YK(杜英魁)
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| 刊名 | Pattern Recognition
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| 出版日期 | 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 |
| 产权排序 | 2 |
| 通讯作者 | 范保杰 |
| 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 |
| 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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