电光与控制, 2019, 26 (10): 87, 网络出版: 2021-01-31
基于YOLOv3目标跟踪方法
YOLOv3 Based Object Tracking Method
摘要
提出了一种基于深度学习检测算法YOLOv3的目标跟踪算法。该算法利用深度学习模型在目标特征提取上的优势, 采用基于回归的YOLOv3检测模型提取候选目标, 同时结合目标颜色直方特征和局部二值模式直方特征进行目标筛选, 实现对目标的跟踪。为了提高算法的性能, 还提出了一种K邻域搜索方法, 可以针对选定目标进行邻域检测。实验结果表明, 提出的目标跟踪算法跟踪效果很好, 综合表现比4种对比算法提高了80%左右, 同时在目标物体光照变化、姿态变化、尺寸变化、旋转变化等复杂情况下有很好的鲁棒性。
Abstract
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 timea 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.
李晶, 黄山. 基于YOLOv3目标跟踪方法[J]. 电光与控制, 2019, 26(10): 87. LI Jing, HUANG Shan. YOLOv3 Based Object Tracking Method[J]. Electronics Optics & Control, 2019, 26(10): 87.