Enhancing Person Re-identification by Robust Structural Metric Learning
文献类型：会议论文
| 作者 | Gang Yuan; Zhaoxiang Zhang ; Yunhong Wang
|
| 出版日期 | 2013-07-26 |
| 会议日期 | 26-28 July 2013 |
| 会议地点 | Qingdao, China |
| 关键词 | Robust Person Re-identification Structural Metric Learning Input Sparsity |
| 英文摘要 | Person re-identification has become an important but also challenging task for video surveillance systems as it aims to match people across non-overlapping camera views. So far, most successful methods either focus on robust feature representation or sophisticated learners. Recently, metric learning has been applied in this task which aims to find a suitable feature subspace for matching samples from different cameras. However, most metric learning approaches rely on either pair wise or triplet-based distance comparison, which can be easily over-fitting in large scale and high dimension learning situation. Meanwhile, the performance of these methods can significantly decrease when the extracted features contain noisy information. In this paper, we propose a robust structural metric learning model for person re-identification with two main advantages: 1) it applies loss functions at the level of rankings rather than pair wise distances, 2) the proposed model is also robust to noisy information of the extracted features. The approach is verified on two available public datasets, and experimental results show that our method can get state-of-the-art performance. |
| 会议录 | ICIG 2013
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13288]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Zhaoxiang Zhang |
| 推荐引用方式 GB/T 7714 | Gang Yuan,Zhaoxiang Zhang,Yunhong Wang. Enhancing Person Re-identification by Robust Structural Metric Learning[C]. 见:. Qingdao, China. 26-28 July 2013. |
入库方式： OAI收割
来源：自动化研究所
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