Attribute Feature Fusion Network for Pedestrian Detection and Re-Identification

Person re-identification is presented to retrieve specific pedestrian targets across monitoring devices. In this paper, we propose an attribute feature fusion network (AFFNet) for pedestrian detection and re-identification, which integrates pedestrian detection, person re-identification and attribute recognition into a deep network for end-to-end multi-task learning. In AFFNet, we design the identity and attribute branches to separately learn the identity and attribute fusion features of pedestrians. The identity features focus on the overall pedestrian appearance, while the attribute fusion features pay attention to local regions of the pedestrian. These two types of features are concatenated for the person re-identification task, which complement each other to provide a more comprehensive description of pedestrians. Experimental results on two commonly used person re-identification datasets including DukeMTMC-reID and Market-1501 demonstrate the effectiveness of proposed AFFNet.