In recent years, Multi-Camera Multiple Object Tracking (MCMT) has gained significant attention as a crucial computer vision application. Research focuses on data association and track detection. However, accurately selecting datasets from raw vision data remains challenging due to real-world complexities like object types, varying speeds, and unknown directions. To address these problems, this paper proposes the Object Tracking Model (OTM) to capture the feature of target area with the Camera Monitoring Network (CMN) based on Graph Convolutional Network (GCN). Our method gives the way for many existing MCMT method to apply into real applications, especially the large scale CMN, which usually provides quite huge amount of raw data, and can reduce the time consumption on the object detection from the data set. Experimental results show that our method outperform existing MC-MOT algorithms by a large margin on CityFlowV2 datasets.