CASIA OpenIR  > 智能感知与计算研究中心
Pose Guided Deep Model for Pedestrian Attribute Recognition in Surveillance Scenarios
Li Dangwei; Chen Xiaotang; Zhang Zhang; Huang Kaiqi
2018
会议名称International Conference on Multimedia and Expo
会议日期July 23-27, 2018
会议地点San Diego, USA
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. 
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/22077
专题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],2018.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
icme18.pdf(880KB)会议论文 开放获取CC BY-NC-SA浏览 请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Li Dangwei]的文章
[Chen Xiaotang]的文章
[Zhang Zhang]的文章
百度学术
百度学术中相似的文章
[Li Dangwei]的文章
[Chen Xiaotang]的文章
[Zhang Zhang]的文章
必应学术
必应学术中相似的文章
[Li Dangwei]的文章
[Chen Xiaotang]的文章
[Zhang Zhang]的文章
相关权益政策
暂无数据
收藏/分享
文件名: icme18.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。