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.

人员重新识别（Re-ID）是智能视频监控中的一项重要技术。由于摄像机视点和身体姿势的变化，存在一些问题，例如身体未对准，背景杂乱和部分身体遮挡等。为了解决这些问题，我们提出了一种新颖的全局局部背景偏置网络（GLBN）网络架构，由前景部分分段网（FPSN），全局对齐监管网（GASN）和Background_bias约束网（BCN）模块组成。首先，为了增强前景特征的适应性并减少背景干扰，应用FPSN对前景图像进行局部分割。其次，GASN生成的全局特征旨在监督局部特征的学习。最后，BCN约束背景信息以再次减少背景信息的影响。在包括Market1501，DukeMTMC-reID和CUHK03在内的主流评估数据集上进行的大量实验表明，我们的方法是有效且稳健的。