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. 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. 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. 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.