| Person Re-Identification by Regularized Smoothing KISS Metric Learning | |
Tao, Dapeng1; Jin, Lianwen1; Wang, Yongfei1; Yuan, Yuan2 ; Li, Xuelong2
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| 2013-10-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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| 卷号 | 23期号:10页码:1675-1685 |
| 摘要 | With the rapid development of the intelligent video surveillance (IVS), person re-identification, which is a difficult yet unavoidable problem in video surveillance, has received increasing attention in recent years. That is because computer capacity has shown remarkable progress and the task of person re-identification plays a critical role in video surveillance systems. In short, person re-identification aims to find an individual again that has been observed over different cameras. It has been reported that KISS metric learning has obtained the state of the art performance for person re-identification on the VIPeR dataset [39]. However, given a small size training set, the estimation to the inverse of a covariance matrix is not stable and thus the resulting performance can be poor. In this paper, we present regularized smoothing KISS metric learning (RS-KISS) by seamlessly integrating smoothing and regularization techniques for robustly estimating covariance matrices. RS-KISS is superior to KISS, because RS-KISS can enlarge the underestimated small eigenvalues and can reduce the overestimated large eigenvalues of the estimated covariance matrix in an effective way. By providing additional data, we can obtain a more robust model by RS-KISS. However, retraining RS-KISS on all the available examples in a straightforward way is time consuming, so we introduce incremental learning to RS-KISS. We thoroughly conduct experiments on the VIPeR dataset and verify that 1) RS-KISS completely beats all available results for person re-identification and 2) incremental RS-KISS performs as well as RS-KISS but reduces the computational cost significantly. |
| 文章类型 | Article |
| 关键词 | Incremental Learning Intelligent Video Surveillance Metric Learning Person Re-identification |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TCSVT.2013.2255413 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | DISCRIMINANT-ANALYSIS ; FACE RECOGNITION ; FEATURES ; CLASSIFICATION ; REDUCTION ; VARIABLES ; ENSEMBLE ; TRACKING ; CAMERAS ; SCALE |
| 语种 | 英语 |
| WOS研究方向 | Engineering |
| WOS类目 | Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000325662200004 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/23481 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Guangdong, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tao, Dapeng,Jin, Lianwen,Wang, Yongfei,et al. Person Re-Identification by Regularized Smoothing KISS Metric Learning[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2013,23(10):1675-1685. |
| APA | Tao, Dapeng,Jin, Lianwen,Wang, Yongfei,Yuan, Yuan,&Li, Xuelong.(2013).Person Re-Identification by Regularized Smoothing KISS Metric Learning.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,23(10),1675-1685. |
| MLA | Tao, Dapeng,et al."Person Re-Identification by Regularized Smoothing KISS Metric Learning".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 23.10(2013):1675-1685. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Person Re-Identifica（896KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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