A hybrid real-time visual tracking using compressive RGB-D features
文献类型：会议论文
| 作者 | Zhao, Mengyuan1; Luo, Heng2; Tafti, Ahmad P.3; Lin, Yuanchang4; He, Guotian4
|
| 出版日期 | 2015 |
| 会议日期 | December 14, 2015 - December 16, 2015 |
| 会议地点 | Las Vegas, NV, United states |
| DOI | 10.1007/978-3-319-27857-5_51 |
| 页码 | 561-573 |
| 通讯作者 | He, Guotian (heguotian@cigit.ac.cn) |
| 英文摘要 | The online multi-instance learning tracking (MIL) algorithm is known for its ability of alleviating tracking drift by training classifiers with positive and negative bag. However, the increased computational complexity results in time consuming due to the lack of consideration of sampling importance when collecting training samples. Additionally, the MIL method, as a 2D feature-based tracking algorithm, performs unsteadily when the object changes poses or rotates seriously. In this paper, a histogram-based feature similarity measurement is employed as a weighting strategy to select positive samples. Benefited from profitable depth information, the tracking algorithm we proposed achieves higher tracking performance. For computational efficiency, a compressive sensing method is adopted to extract features and reduce dimensionality. Experimental results demonstrate that our algorithm is better in robustness, accuracy, efficiency than three state-of-the-art methods on challenging video sequences. © Springer International Publishing Switzerland 2015. |
| 会议录 | 11th International Symposium on Advances in Visual Computing, ISVC 2015
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| 语种 | 英语 |
| 电子版国际标准刊号 | 16113349 |
| ISSN号 | 03029743 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/4815]  |
| 专题 | 机器人与3D打印技术创新中心 |
| 作者单位 | 1.College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China; 2.College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China; 3.Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee; WI, United States; 4.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China |
| 推荐引用方式 GB/T 7714 | Zhao, Mengyuan,Luo, Heng,Tafti, Ahmad P.,et al. A hybrid real-time visual tracking using compressive RGB-D features[C]. 见:. Las Vegas, NV, United states. December 14, 2015 - December 16, 2015. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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