Correlation Graph Convolutional Network for Pedestrian Attribute Recognition

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Correlation Graph Convolutional Network for Pedestrian Attribute Recognition

Haonan Fan; Hai-Miao Hu; Shuailing Liu; Weiqing Lu; Shiliang Pu

The pedestrian attribute recognition aims at generating the structured description of pedestrian, which plays an important role in surveillance. However, it is difficult to achieve accurate recognition results due to diverse illumination, partial body occlusion and limited resolutions. Therefore, this paper proposes a comprehensive relationship framework for comprehensively describing and utilizing relations among attributes, describing different type of relations in the same dimension, and implementing complex transfers of relations in a GCN manner. This framework is named Correlation Graph Convolutional Network (CGCN). Based on the proposed framework, the feature vectors are built to associate attributes with image features and generate different relation matrices through self-attention among different feature vectors, describing different attribute relations. Then, we conduct multi-layer transfer of attribute relations by means of graph convolution, realizing complex utilization of attribute relations. In addition, the relations among attributes are fully exploited and two types of relations, namely the explicit and implicit relations, are proposed to be integrate into the proposed comprehensive relationship framework. The experimental results on RAP and PETA demonstrate that the recognition performance of the proposed CGCN can obviously outperform the state-of-the-arts, and moreover, the CGCN can achieve a better synergy with different relations.

SPS on Twitter

  • New SPS Webinar: On 9 March, join Mr. Sayantan Dutta when he presents "Novel Prospects of Image Restoration Inspire…
  • New SPS Webinar: On Wednesday, 8 February, join Dr. Roula Nassif for "Decentralized learning over multitask graphs"…
  • CALL FOR PAPERS: IEEE Signal Processing Magazine welcomes submissions for a Special Issue on Hypercomplex Signal an…
  • New SPS Webinar: On 15 February, join Mr. Wei Liu, Dr. Li Chen and Dr. Wenyi Zhang presenting "Decentralized Federa…
  • New SPS Webinar: On Monday, 13 February, join Dr. Joe (Zhou) Ren when he presents "Human Centric Visual Analysis -…

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar