Every detection box for multiple object tracking based on GAM

Multiple object tracking (MOT) has become a very important task in the field of rail transit. Recently, the most effective method in MOT tasks is tracking by detection. Because there are many pedestrians under a single camera in the railway station, the occluded pedestrians will generate detection boxes with low-score. Once these low-score bounding boxes are mistakenly classified as background by the detector, the traditional tracking model may discard these boxes, resulting in poor tracking effect. In order to solve this problem effectively, this paper proposes a novel model with Generic Association Mechanism (GAM), which can pay attention to every detection box instead of only focusing on the highscore ones. This method utilizes the similarity between these low-score detection boxes and trajectories to restore true objects and filter out the background detection boxes. More attention can be focused on these low-score detection boxes through this method, which contribute the most to the tracking effect. This method can efficiently avoid the loss of true objects and fragmented trajectories of occluded objects. Finally, we test this proposed model with other models on several public datasets, and the results show that the proposed method achieves more significant performance improvement.