遮挡行人重识别是一项具有挑战性的计算机视觉任务。提出了一种FGMS-Net网络方法，通过多个方面的改进显著提升了遮挡环境下的行人重识别能力。首先，采用改进的前景分割技术，有效地去除背景和其他杂波信息，使得特征提取更加精确。其次，针对遮挡问题，引入多尺度特征判别的方法，使得模型能够更好地捕捉局部特征，从而增强识别能力。最后，在主干网络中添加注意力机制，使得网络能够更加关注关键信息，提高整体识别性能。实验结果表明，所提方法在遮挡行人重识别任务中取得了显著的性能提升，在Occluded-DukeMTMC数据集上，累积匹配特征Rank-1和平均精度均值（mean average precision, mAP）分别达到了71.7%和61.6%。

Occluded pedestrian re-identification is a challenging task in the field of computer vision. A method was proposed using the FGMS-Net network, which significantly enhances pedestrian re-identification in occluded environments through several improvements. Firstly, an improved foreground segmentation technique was employed to effectively remove background and other clutter information, resulting in more accurate feature extraction. Secondly, to address the occlusion issue, a multi-scale feature discrimination method was introduced, enabling the model to better capture local features and thereby enhancing identification capability. Finally, an attention mechanism was added to the backbone network, allowing the network to focus more on critical information and improve overall recognition performance. The experimental results show that method proposed has achieved significant performance improvement in the task of pedestrian re recognition with occlusion. On the Occluded-DukeMTMC dataset, the cumulative matching feature Rank-1 and mean average precision (mAP) reach 71.7% and 61.6%, respectively.