CASIA OpenIR  > 智能感知与计算
Pose Guided Deep Model for Pedestrian Attribute Recognition in Surveillance Scenarios
Li Dangwei; Chen Xiaotang; Zhang Zhang; Huang Kaiqi
Conference NameInternational Conference on Multimedia and Expo
Conference DateJuly 23-27, 2018
Conference PlaceSan Diego, USA
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. 
Indexed ByEI
Document Type会议论文
Affiliation1.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
Recommended Citation
GB/T 7714
Li Dangwei,Chen Xiaotang,Zhang Zhang,et al. Pose Guided Deep Model for Pedestrian Attribute Recognition in Surveillance Scenarios[C],2018.
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