Re-identification assistance and multi-stage association for pedestrian multi-object tracking

The main task of pedestrian multi-object tracking is to continuously detect and locate target pedestrians in a video sequence and complete inter-frame target association, which has broad application prospects in various fields such as intelligent surveillance and video analysis. Currently, most methods mainly use the Kalman filter to model the motion and predict the position of the pedestrian in the next frame. Then, based on the Intersection over Union (IoU) between the predicted bounding box and the detection bounding box, the Hungarian algorithm is used to match the targets between frames. However, data association methods that rely solely on spatial information, such as IoU, cannot ensure the consistency of pedestrian identity when severe occlusion occurs, nor can they guarantee the stability of identity when different pedestrians are in close proximity. Pedestrian appearance information can effectively address the above problems. Even when the pedestrian's position is lost for a certain number of frames due to occlusion, the appearance information of the pedestrian remains consistent before and after occlusion. Therefore, we design a multi-object pedestrian tracking method that combines re-identification feature assistance and multi-stage data association (RAMA). This method innovatively focuses on the role of low confidence bounding boxes in MOT, and introduces a separately trained pedestrian re-identification model to extract discriminative features of pedestrians, then adds this feature to the multi-stage data association algorithm to improve the accuracy of multi-object tracking. The RAMA exhibits stronger identity association ability on the MOT16 and MOT17 test sets, achieving an IDF1 of 75.0% and 74.5%, respectively.