Person Re-Identification by Regularized Smoothing KISS Metric Learning
文献类型：期刊论文
| 作者 | Tao, Dapeng1; Jin, Lianwen1; Wang, Yongfei1; Yuan, Yuan2 ; Li, Xuelong2
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| 刊名 | ieee transactions on circuits and systems for video technology
 |
| 出版日期 | 2013-10-01 |
| 卷号 | 23期号:10页码:1675-1685 |
| 关键词 | Incremental learning intelligent video surveillance metric learning person re-identification |
| 英文摘要 | 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. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | engineering, electrical & electronic |
| 研究领域[WOS] | engineering |
| 关键词[WOS] | discriminant-analysis ; face recognition ; features ; classification ; reduction ; variables ; ensemble ; tracking ; cameras ; scale |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000325662200004 |
| 公开日期 | 2015-06-30 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/23481]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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