Deep Network with Spatial and Channel Attention for Person Re-identification

Abstract

Most existing person re-identification (Re-ID) methods assume pedestrian images are well-aligned within tightly surrounded bounding boxes or require additional annotation information to calibrate misaligned images. In this work, we propose a novel deep network to address the misalignment problem in person re-identification task without requiring additional annotation. Spatial attention selection mechanism is introduced in our network to align the pedestrian images. Moreover, we present a channel attention selection mechanism to integrate the global image feature maps and regional feature maps more effectively by explicitly modelling interdependencies between channels and recalibrates feature response in each channel. Extensive experiments and comparative evaluations demonstrate the effectiveness of our approach and the superiority of this novel network for person re-identification over a wide variety of state-of-the-art methods on two datasets including Market-1501 and CUHK03(both detected and labeled sets).