Abstract: The environment of railway passenger stations is complex and the passenger flow is dense. Once important events involving passenger safety and affecting station operations occur, passenger transport staff urgently need to quickly grasp the station trajectory of relevant passengers. Therefore, this paper designed a pedestrian reidentification model based on self-supervised part perception, and could implement real-time tracking of key passengers at railway passenger stations based on this model. The paper elaborated on the architecture of the model from two aspects: self-supervised part perception pre training and pedestrian re recognition transfer learning. Experiments have shown that the performance of this model surpasses the general pedestrian reidentification model on various types of pedestrian reidentification datasets, especially those with severe occlusion. The on-site trial at Baiyin South Station of China Railway Lanzhou Group Co. Ltd. shows that the model can effectively track the trajectory of key passengers inside the railway passenger station, and provide technical support for passenger related work.