Which Part is Better: Multi-Part Competition Network for person Re-Identification

Abstract

Person re-identification is a challenging task due to the background clutters, occlusion and illumination variations. In addition, the pedestrian misalignment always exists in some automatic-detection datasets. In this paper, we propose a Multi-Part Competition Network (MPCN) consisting of Multi-Part Network (MPN) and Part Competition Network (PCN), which aims to solve the misalignment problem caused by the detector errors and human pose variations. First, we construct original body parts and enlarged body parts using human pose estimation algorithm. These two kinds of body parts not only alleviate the misalignment from background and varying human pose but also solve the missing details and imprecise body parts introduced by human pose estimator. Then, we use MPN to acquire global features and two different body parts features. The components of MPN, a global branch and two part branches, are combined by ROI pooling layer. Finally, we apply PCN to achieve a tradeoff between the original body parts and the enlarged body parts and acquire discriminative part features from these two different body parts. Extensive evaluations on three widely used re-id datasets, Market-1501, CUHK03, VIPeR demonstrate that our proposed network have a competitive result compared to the state-of-the-art methods.