联合YOLO和Camshift的目标跟踪算法研究

Research on Target Tracking Algorithm Based on YOLO and Camshift

为了解决传统目标跟踪算法在有遮挡后无法准确跟踪的问题,提出了将YOLO和Camshift算法相联合的目标跟踪算法.基于YOLO网络结构来构建目标检测的模型,在模型构建之前,采用图像增强的方法对视频帧进行预处理,在保留视频帧中足够图像信息的同时,提高图像质量,降低YOLO算法的时间复杂度.用YOLO算法确定出目标,完成对目标跟踪的初始化.根据目标的位置信息使用Camshift算法对后续的视频帧进行处理,并对每一帧的目标进行更新,从而可以保证不断调整跟踪窗口位置,适应目标的移动.实验结果表明,所提的方法能够有效地克服目标被遮挡后跟踪丢失的问题,具有很好的鲁棒性.

In order to solve the problem that traditional target tracking cannot be accurately tracked after occlusion,a target tracking algorithm combining YOLO and Camshift algorithm is proposed.Building a model of target detection using YOLO network structure,before the model is constructed,the image frame is preprocessed by image enhancement method,while maintaining sufficient image information in the video frame,improving the image quality and reducing the time complexity of the YOLO algorithm.The target is determined by the YOLO algorithm,and the initialization of the target tracking is completed.According to the position information of the target,the Camshift algorithm is used to process the subsequent video frames,and the target of each frame is updated,so that the position of the search window can be continuously adjusted to adapt to the movement of the target.The experimental results show that the proposed method can effectively overcome the problem of tracking loss after the target is occluded,and has good robustness.