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.

多目标跟踪是计算机视觉中最基本的任务之一，由于其严重的遮挡和运动模糊，它对于现实世界的应用仍然非常具有挑战性。现有的大多数方法通过基于连续帧中检测的深度特征进行数据关联来解决这些多目标跟踪问题，其中仅包含被检测目标的空间信息。因此，很容易出现数据关联的不准确，尤其是在严重遮挡的场景中。在本文中，提出了一种名为序列跟踪器（STracker）的新型多目标跟踪模型，它结合了时间和空间特征来执行数据关联。我们离线训练了一个基于视频行人重识别的序列特征提取网络，将得到的序列特征与前一帧的深度特征融合，然后实现匈牙利算法进行数据关联。已经进行了实验以验证所提出算法的有效性，相应的结果表明它可以显着提高本文数据集的轨迹质量。值得注意的是，对于MOT官网公开的检测器结果，该算法在MOT17数据集上可以达到57.2%的MOTA和50.9%的IDF1。已经进行了实验以验证所提出算法的有效性，相应的结果表明它可以显着提高本文数据集的轨迹质量。值得注意的是，对于MOT官网公开的检测器结果，该算法在MOT17数据集上可以达到57.2%的MOTA和50.9%的IDF1。已经进行了实验以验证所提出算法的有效性，相应的结果表明它可以显着提高本文数据集的轨迹质量。值得注意的是，对于MOT官网公开的检测器结果，该算法在MOT17数据集上可以达到57.2%的MOTA和50.9%的IDF1。