The goal of the Multi-Target Multi-Camera (MTMC) pedestrian tracking task is to simultaneously track multiple target individuals using multiple cameras. Current methods mostly use exponential moving averages to store features and perform multi-camera person matching using these features. Apparently, it will cause the issue of poor long-term feature storage, in this paper, we propose a new method to address the issue, which often leads to ID-switching when individuals change clothes or when lighting conditions change significantly, and also improve the ID-switching problem that occurs during single-camera tracking. To evaluate our method, we created our owns dataset. The dataset was included approximately 40000 frames from 1080p, 30fps videos, whitch were recorded by these cameras. Experimental results show that our method outperforms existing methods in both single-camera and multi-camera tracking, with single-camera tracking improved by 7.06% and multi-camera tracking improved by 12.51%.