Pose-driven deep convolutional model for person re-identification
文献类型：会议论文
| 作者 | Chi Su1; Jianing Li1; Shiliang Zhang1; Junliang Xing2 ; Wen Gao1; Qi Tian3
|
| 出版日期 | 2017 |
| 会议日期 | 2017 |
| 会议地点 | Venice, Italy |
| 英文摘要 | Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end to end. Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts. To match the features from global human body and local body parts, a pose driven feature weighting sub-network is further designed to learn adaptive feature fusions. Extensive experimental analyses and results on three popular datasets demonstrate significant performance improvements of our model over all published state-of-the-art methods. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20067]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_视频内容安全团队 |
| 作者单位 | 1.School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 3.Department of Computer Science, University of Texas at San Antonio, San Antonio |
| 推荐引用方式 GB/T 7714 | Chi Su,Jianing Li,Shiliang Zhang,et al. Pose-driven deep convolutional model for person re-identification[C]. 见:. Venice, Italy. 2017. |
入库方式： OAI收割
来源：自动化研究所
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