Person Re-identification with Global-Local Background_bias Net

Abstract

Person Re-identification (Re-ID) is an important technique in intelligent video surveillance. Because of the variations on camera viewpoints and body poses, there are some problems such as body misalignment, the diverse background clutters and partial bodies occlusion, etc. To address these problems, we propose the Global-Local Background_bias Net (GLBN), a novel network architecture that consists of Foreground Partial Segmentation Net (FPSN), Global Aligned Supervision Net (GASN) and Background_bias Constraint Net (BCN) modules. Firstly, to enhance the adaptability of foreground features and reduce the interference of the background, FPSN is applied to perform local segmentation on the foreground image. Secondly, global features generated by GASN are purposed to supervise the learning of local features. Finally, BCN constrains the background information to reduce the impact of background information again. Extensive experiments implemented on the mainstream evaluation datasets including Market1501, DukeMTMC-reID and CUHK03 indicate that our method is efficient and robust.