Close-set camera style distribution alignment for single camera person re-identification

Abstract

The purpose of person re-identification (ReID) is to find the same person under different cameras and the basic difficulty lies in the need for large amounts of cross-camera pedestrian annotations. In reality, annotating cross-camera pedestrians is time-consuming especially in large-scale surveillance camera networks. This paper focuses on addressing the ReID problem under single-camera training (SCT) setting, where each person of the training set only appears in one camera. Due to the lack of cross-camera pedestrian annotations, it is difficult to effectively eliminate the camera style interference by narrowing the distance between the image features of the same person. To address this problem, we propose a close-set camera style distribution alignment (C2SDA) framework for SCT ReID. In order to reduce the camera-style interference from both instance- and distribution-levels simultaneously, we first design an instance-distribution camera style alignment module that directly aligns the feature distribution of input images under each camera and then trains the model at instance level with the aligned features. Secondly, we further design an augment close-set camera style distribution module that transforms the camera feature distribution alignment problem from open-set into close-set one, which preserving the discriminative ability of features during the alignment process. Experimental results verify that our framework can significantly improve the ReID performance under SCT setting and surpass the current SOTA methods. The source code is available athttps://github.com/HongweiZhang97/CCSDA.