Pose Guided Deep Model for Pedestrian Attribute Recognition in Surveillance Scenarios | |
Li Dangwei; Chen Xiaotang; Zhang Zhang; Huang Kaiqi | |
2018 | |
会议日期 | July 23-27, 2018 |
会议地点 | San Diego, USA |
英文摘要 |
Recognizing pedestrian attributes, such as gender, backpack, and cloth types, has obtained increasing attention recently due to its great potential in intelligent video surveillance. Existing methods usually solve it with end-to-end multi-label deep neural networks, while the structure knowledge of pedestrian body has been little utilized. Considering that attributes have strong spatial correlations with human structures, e.g. glasses are around the head, in this paper, we introduce pedestrian body structure into this task and propose a Pose Guided Deep Model (PGDM) to improve attribute recognition. The PGDM consists of three main components: 1) coarse pose estimation which distillates the pose knowledge from a pre-trained pose estimation model, 2) body parts localization which adaptively locates informative image regions with only image-level supervision, 3) multiple features fusion which combines the part-based features for attribute recognition. In the inference stage, we fuse the part-based PGDM results with global body based results for final attribute prediction and the performance can be consistently improved. Compared with state-of-the-art models, the performances on three large-scale pedestrian attribute datasets, i.e., PETA, RAP, and PA-100K, demonstrate the effectiveness of the proposed method. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/22077] |
专题 | 自动化研究所_智能感知与计算研究中心 |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.CAS Center for Excellence in Brain Science and Intelligence Technology 4.National Laboratory of Pattern Recognition 5.Center for Research on Intelligent Perception and Computing |
推荐引用方式 GB/T 7714 | Li Dangwei,Chen Xiaotang,Zhang Zhang,et al. Pose Guided Deep Model for Pedestrian Attribute Recognition in Surveillance Scenarios[C]. 见:. San Diego, USA. July 23-27, 2018. |
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