基于图像矩信息的CamShift视觉跟踪方法

为了适应视觉跟踪过程中目标形态的变化,使用核密度估计对视频序列中的运动目标进行色彩分布建模,运用CamShift算法进行跟踪,并结合图像矩信息确定目标区域.采用全局更新策略对目标色彩分布模型进行实时更新,进一步提高了跟踪的准确性.实验结果表明,该方法对目标在平移、旋转、局部遮挡等不同运动条件下均可实现稳定的跟踪,克服了尺度变化对跟踪带来的影响,是一种鲁棒性较强的跟踪算法.

CamShift Tracker Based on Image Moments

Kernel density estimation is first used to establish the color distribution model of a moving target in video sequences to adapt the change of scale.CamShift algorithm is then used to track the object,and its region is located by integrating image moments.To improve the tracking precision,a global update approach is applied to the color distribution model in real time.Experimental results show that the method,which has a strong robustness,can achieve stable tracking when moving object under the conditions of translation,rotation,and partial occlusion,and effectively overcome the impact of scale change.