目的 目标的长距离跟踪一直是视频监控中最具挑战性的任务之一。现有的目标跟踪方法在存在遮挡、目标消失再出现等情况下往往会丢失目标，无法进行持续有效的跟踪。一方面目标消失后再次出现时，将其作为新的目标进行跟踪的做法显然不符合实际需求；另一方面，在跟踪过程中当相似的目标出现时，也很容易误导跟踪器把该相似对象当成跟踪目标，从而导致跟踪失败。为此，提出一种基于目标识别辅助的跟踪算法来解决这个问题。方法 将跟踪问题转化为寻找帧间检测到的目标之间对应关系问题，从而在目标消失再现后，采用深度学习网络实现有效的轨迹恢复，改善长距离跟踪效果，并在一定程度上避免相似目标的干扰。结果 通过在标准数据集上与同类算法进行对比实验，本文算法在目标受到遮挡、交叉运动、消失再现的情况下能够有效地恢复其跟踪轨迹，改善跟踪效果，从而可以对多个目标进行持续有效的跟踪。结论 本文创新性地提出了一种结合基于深度学习的目标识别辅助的跟踪算法，实验结果证明了该方法对遮挡重现后的目标能够有效的恢复跟踪轨迹，适用在监控视频中对多个目标进行持续跟踪。

Objective Long-distance tracking is an important and challenging task in video surveillance. Existing tracking methods may fail when a target is completed occluded and is treated as a new target upon reappearance. Moreover, trackers are often confused by targets that appear similar. To address these problems, we propose a tracking algorithm that is aided by target recognition based on deep learning.Methods The proposed method solves problems with tracking by identifying the corresponding relationship of objects detected between different frames. When an old target reappears, the algorithm can resume its tracking trajectory based on deep learning networks. Hence, the performance of tracking multiple and similar targets is improved.Results Experiments were conducted by comparing the standard dataset with other algorithms. Results showed that the proposed method can address occlusion, overlapping, and improve the performance of long-distance tracking. Therefore, the proposed method can continuously and effectively perform tracking.Conclusion We propose a novel object tracking algorithm that is aided by recognition based on deep learning. The experimental results demonstrated the advantages of the proposed method in addressing the problem of a completely occluded object. Therefore, the proposed algorithm is suitable for the continuous tracking of multiple targets in monitoring videos.