摘要
Meanshift是一种目标跟踪的有效算法。但是,当光照条件变化快或目标被遮挡的时候表现很差。与之相比,基于粒子滤波的目标跟踪有一个很好的表现,但是跟踪速度比Meanshift慢很多。由于使用单个算法的限制,本文提出了一种基于Meanshift和粒子滤波相结合的新的算法。此种方法构建了反馈系统,Meanshift技术被用于初始跟踪,当Meanshift的跟踪结果不可信时,通过粒子滤波来提高跟踪效果。RGB颜色直方图用于表征图像的特征,Bhattacharyya系数来衡量目标模型与候选模型的相似度。通过对不同视频的跟踪实验证明,提出的这种方法在目标发生移变、旋转、缩放时都能很好的表现,而且实现了一个满意的跟踪速度。
Meanshift is an effective algorithm for object tacking. However,it has a poor performance when the illumination condition changes fast or the tracking target are shadowed. By contract,particle filter based object tracking has a better tracking performance,but the tracking speed is much slower compared to mean-shift.Owing to the limitations of the use of a single algorithm,a novel object tracking method based on both meanshift and particle filter is proposed in this paper. A system with feedback has been constructed by the proposed method,in which the mean-shift technique is used for initial registration and the particle filter is called to improve the performance of tracking when the tracking result with meanshift is unconvincing. RGB color histogram is exploited as image feature and Bhattacharyya coefficient is used for measuring the similarity between object model and candidate regions. Tracking experiments on various videos show that the proposed method performs well and achieves a satisfying tracking speed when targeted objects go through shift-variant,rotation and scaling.
出处
《湖北第二师范学院学报》
2016年第2期22-26,共5页
Journal of Hubei University of Education
基金
湖北省教育厅科学技术研究计划优秀中青年人才项目资助(Q20121409)
