Learning Convolution Feature Aggregation via Edge Attention Convolution Network for Person Re-Identification

Chaoqun Lin, Ruo-Pei Guo, Mingkun Li, Xianbiao Qi, Chun-Guang Li

Abstract: Person Re-Identification (Re-ID) is a challenging task of matching pedestrian images collected from nonoverlapping multiple camera views due to huge variations from pose changes, occlusions, varying illumination and clutter background. Recently, graph convolution network or graph neural network increasingly gains a lot of research attention in person Re-ID. However, the existing methods have not fully exploit the available features on the graph. In this paper, we propose an efficient and effective end-to-end trainable framework, termed Edge Attention Convolution Network (EACN), to perform convolution feature learning and attentive feature aggregation for person Re-ID, in which the learned convolution features on vertex and its edges are attentively aggregated on a dynamic graph. We conduct extensive experiments on two large benchmark datasets, Market-1501 and DukeMTMC. Experimental results validate the efficiency and effectiveness of our proposal.