Dual attention and part drop network for person reidentification

Abstract

Pedestrian occlusion, variations in the cross-view angle, and the appearances of pedestrians significantly hinder person reidentification (ReID). A dual attention and part drop network (DAPD-Net) for person ReID is proposed. The dual attention module enables the deep neural network to focus on the pedestrian in the foreground of a given image and weakens background perturbance. It can speed up learning and improve network performance. Feature maps in the part drop branch that we proposed are divided into multiple parts, one of them is randomly dropped, and the remainder are learned to obtain a feature that is robust against occlusion. Through part drop training, the antiocclusion ability of the network is effectively improved. The middle-layer branch is used, which help our network to learn mid-level semantic feature and promote capability of the system. These innovative modules can help deep neural network to extract discriminative feature representations. We conduct extensive experiments on multiple public datasets of person ReID. The results show that our method outperforms many state-of-the-art methods.