An object tracking algorithm is proposed based on the deep learning detection algorithm of YOLOv3 (YOLOv3:An Incremental Improvement), which utilizes the advantages of deep learning model in target feature extraction, and extracts candidate targets by using regression-based YOLOv3 detection model. The target color histogram feature and Local Binary Pattern (LBP) feature are also used for target screening, thus to implement object tracking.At the same timea method called K-neighbor searching is presented to improve algorithm performance, which performs neighborhood detection for the selected targets. Experimental results show that the proposed algorithm has a good tracking performance, with an overall performance improved by about 80% in comparison with the four contrast algorithms, and has good robustness in the complex situations of illumination changing, posture changing, size changing and rotation of target object.