多目标跟踪是计算机视觉领域的一个重要分支，得益于当前计算机硬件以及深度学习技术的飞速发展，基于深度学习的多目标跟踪算法取得了显著进展和亮眼成绩。为了推动视觉多目标跟踪算的研究发展，通过汇总近年来本领域创新成果，对当前研究进展进行综合论述。在介绍了多目标跟踪的背景和应用场景的基础上，分别从基于检测的多目标跟踪算法、联合检测与跟踪的多目标跟踪算法、基于Transformer的多目标跟踪算法和融合语义指引的多目标跟踪算法4个方面阐述了当前的研究进展；汇总常用基准数据集和多跟踪算法的评价指标，并将本文算法在常用基准数据上进行对比分析；最后对基于深度学习的视觉多目标跟踪算法的未来发展进行展望。

Multi-object tracking is an important branch in the field of computer vision. Owing to the rapid development of computer hardware and deep learning technology, significant progress has been made in deep learning-based multi-object tracking, yielding remarkable results. To promote the research progress in the field of visual multi-object tracking, a comprehensive review of recent innovative outcomes was conducted to discuss the current state of research advancements. On the basis of introducing the background and application scenarios of multi-object tracking, the research progress was discussed in four aspects: tracking by detection, joint detecting and tracking, transformer-based tracking, referring multi-object tracking. Common benchmark datasets and evaluation metrics for multi-tracking algorithms were summarized, and a comparative analysis of the algorithms mentioned was conducted on these datasets. Ultimately, exploring the prospective evolution of deep learning-based visual multi-object tracking, three future research directions were proposed for scholars actively engaged in this field.