Sequence-tracker: Multiple object tracking with sequence features in severe occlusion scene

Abstract

Multiple object tracking is one of the most fundamental tasks in computer vision, and it is still very challenging for real-world applications due to its severe occlusion and motion blur. Most of the existing methods solve these multiple object tracking issues by performing data association based on the deep features of the detections in consecutive frames, which only contain the spatial information of the detected objects. Therefore, the inaccuracy of data association would easily occur, especially in the severe occlusion scenes. In this paper, a novel multiple object tracking model named sequence-tracker (STracker) has been proposed, which combines both the temporal and spatial features to perform data association. We trained a sequence feature extraction network based on video pedestrian re-identification offline, fused the obtained sequence features with the depth features of the previous frame, and then implemented the Hungarian algorithm for data association. Experiments have been carried out to validate the effectiveness of the proposed algorithm and the corresponding results indicates that it can significantly improve the trajectory quality of our dataset in this paper. Remarkably, for the public detector results from MOT official website, the proposed algorithm can achieve up to 57.2% MOTA and 50.9% IDF1 on the MOT17 dataset.