Multi-task Network Learning Representation Features of Attributes and Identity for Person Re-identification

摘要

Person re-identification (re-ID) has become increasingly popular due to its significance in practical application. In most of the available methods for person re-ID, the solutions focus on verification and recognition of the person identity and pay main attention to the appearance details of person. In this paper, we propose multi-task network architecture to learn powerful representation features of attributes and identity for person re-ID. Firstly, we utilize the semantic descriptor on attributes such as gender, clothing details to effectively learn representation features. Secondly, we employ joint supervision of softmax loss and center loss for person identification to obtain deep features with inter-class dispersion and intra-class compactness. Finally, we use the convolutional neural network (CNN) and multi-task learning strategy to integrate the person attributes and identity to complete classifications tasks for person re-ID. Experiments are conducted on Market1501 and DukeMTMC-reID to verify the efficiency of our method.