A part-based attention network for person re-identification

Abstract

Person re-identification (re-id) is the task of recognizing images of the same pedestrian captured by different cameras with non-overlapping views. Person re-id is a challenging task due to the existence of large view variations, such as spatial misalignment, background clutter and human poses change. In this paper, we handle these challenges from the following two aspects: utilizing attention mechanism to alleviate misalignment problem and exploiting the complementary effects of global-local features for more stable pedestrian descriptors. Specifically, we first present a part-based attention model consisting of a channel attention block and a spatial attention block to sequentially refine the convolutional descriptors of person body parts. The channel and spatial attention blocks weight the channels and positions of body-part feature maps to spot the informative channels and regions, respectively. Then global full-body and local body-part of the refined feature maps are pooled into global and local representations, which are jointly trained using identity classification loss. We conduct extensive experiments on four standard benchmark datasets including Market1501, CUHK03, DukeMTMC-reID, and CUHK01, and the experimental results demonstrate the effectiveness of the presented method.