【摘要】 多目标跟踪(Multi-object tracking, MOT)是计算机视觉领域的一项重要任务，现有研究大多针对目标检测和数据关联进行改进，通常忽视了不同帧之间的相关性，未能充分利用视频时序信息，导致算法在运动模糊，遮挡和小目标场景中的性能显著下降。为解决上述问题，本文提出了一种时空特征对齐的多目标跟踪方法。首先，引入卷积门控递归单元(convolutional gated recurrent unit, ConvGRU)，对视频中目标的时空信息进行编码；该结构通过考虑整个历史帧序列，有效提取时序信息，以增强特征表示。然后，设计特征对齐模块，保证历史帧信息和当前帧信息的时间一致性，以降低误检率。最后，本文在MOT17和MOT20数据集上进行了测试，所提算法的MOTA(multiple object tracking accurary)值分别为74.2和67.4，相比基准方法 FairMOT提升了0.5和5.6;IDF1(identification F1 score)值分别为73.9和70.6，相比基准方法 FairMOT提升了1.6和3.3。此外，定性和定量实验结果表明，本文方法的综合跟踪性能优于目前大多数先进方法。更多还原

【Abstract】 Multiple object tracking(MOT) is an important task in computer vision. Most of the MOT methods improve object detection and data association, usually ignoring the correlation between different frames. They don’t make good use of the temporal information in the video, which makes the tracking performance significantly degraded in motion blur, occlusion, and small target scenes. In order to solve these problems, this paper proposes a multiple object tracking method with the aligned spatial-temporal feature. First, the convolutional gated recurrent unit(ConvGRU) is introduced to encode the spatial-temporal information of the object in the video; By considering the whole history frame sequence, this structure effectively extracts the spatial-temporal information to enhance the feature representation. Then, the feature alignment module is designed to ensure the time consistency between the historical frame information and the current frame information to reduce the false detection rate. Finally, this paper tests on MOT17 and MOT20 datasets, and multiple object tracking accuracy(MOTA) values are 74.2 and 67.4,respectively, which is increased by 0.5 and 5.6 compared with the baseline FairMOT method. Our identification F1 score(IDF1) values are 73.9 and 70.6, respectively, which are increased by 1.6 and 3.3 compared with the baseline FairMOT method. In addition, the qualitative and quantitative experimental results show that the overall tracking performance of this method is better than that of most of the current advanced methods.更多还原