Robust visual tracking via augmented kernel SVM
文献类型：期刊论文
| 作者 | Bai, Yancheng; Tang, Ming
|
| 刊名 | IMAGE AND VISION COMPUTING
 |
| 出版日期 | 2014-08-01 |
| 卷号 | 32期号:8页码:465-475 |
| 关键词 | Feature representation Appearance model Augmented Kernel Matrix (AKM) |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology ; Physical Sciences |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Optics |
| 研究领域[WOS] | Computer Science ; Engineering ; Optics |
| 关键词[WOS] | OBJECT TRACKING |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000339039600002 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/2982]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_机器人视觉团队 |
| 作者单位 | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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