Occluded person re-identification (ReID) aims at matching occluded person images to holistic ones across different camera views. Target Pedestrians (TP) are usually disturbed by Non-Pedestrian Occlusions (NPO) and NonTarget Pedestrians (NTP). Previous methods mainly focus on increasing model's robustness against NPO while ignoring feature contamination from NTP. In this paper, we propose a novel Feature Erasing and Diffusion Network (FED) to simultaneously handle NPO and NTP. Specifically, NPO features are eliminated by our proposed Occlusion Erasing Module (OEM), aided by the NPO augmentation strategy which simulates NPO on holistic pedestrian images and generates precise occlusion masks. Subsequently, we Subsequently, we diffuse the pedestrian representations with other memorized features to synthesize NTP characteristics in the feature space which is achieved by a novel Feature Diffusion Module (FDM) through a learnable cross attention mechanism. With the guidance of the occlusion scores from OEM, the feature diffusion process is mainly conducted on visible body parts, which guarantees the quality of the synthesized NTP characteristics. By jointly optimizing OEM and FDM in our proposed FED network, we can greatly improve the model's perception ability towards TP and alleviate the influence of NPO and NTP. Furthermore, the proposed FDM only works as an auxiliary module for training and will be discarded in the inference phase, thus introducing little inference computational overhead. Experiments on occluded and holistic person ReID benchmarks demonstrate the superiority of FED over state-of-the-arts, where FED achieves 86.3% Rank-1 accuracy on Occluded-REID, surpassing others by at least 4.7%.

翻译：隐蔽人的重新定位( ReID) 旨在将隐蔽的个人图像与不同摄像视图中的整体图像相匹配。 目标静地人( TP) 通常受到非佩德斯特人的隐蔽性( NAPO) 和 Nontaget Pedestrians ( NTP ) 的干扰。 先前的方法主要侧重于提高模型对NPO的稳健性,而忽视NTP的特征污染。 在本文中,我们建议建立一个新型的特质 Eviseration and Division 网络( FEDM) 来同时处理NPO 和 NTP 。 具体地说, 我们拟议的隐蔽 EPR 模块( OEM ) 消除了NPO 的隐蔽性隐蔽性( OEM ) 模块( OEM ), 并辅之以NPO 增强战略, 在整体行情图中, 从而保证了 ODPO 的精度 和 FDF IM 的精度 模型 的精度 的精度 。 因此, 我们的精锐化的精细化的精度 将演示的精度 的精度( IMDPDPT ) 的精度( 的精度 ) 的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准性 ODMDDDDDDDDDDDDM 。