A divide-and-unite deep network for person re-identification

Abstract

Person re-identification (person re-ID) is one of the most challenging tasks in the field of computer vision as it involves large variations in human appearances, human poses, background illuminations, camera views, etc. In recent literature, using part-level features for the person re-ID task provides fine-grained information, and has been proven to be effective. Instead of relying on additional skeleton key points or pose estimation models, this paper proposes a Divide-and-Unite Network to obtain feature embedding end-to-end. We design a deep network guided by image contents, which divides pedestrians into parts and obtains the part features with different contributions. These part features and the global feature are united to obtain the pedestrian descriptor for person re-ID. To summarize, the contributions of this work are two-fold. Firstly, a novel architecture of discriminative descriptor learning is proposed, which is based on the global feature and supplemented by part features. Secondly, a Feature Division Network is constructed to generate the part features with different contributions, where the divided parts maintain the consistency of content between different images. Extensive experiments are conducted on three widely-used benchmarks including Market1501, CUHK03, and DukeMTMC-reID. The results have demonstrated that the proposed model can achieve remarkable performance against numerous state-of-the-arts.