基于改进的均值漂移算法的非刚性目标跟踪

针对现有的均值漂移算法不能适应非刚性目标的复杂运动情况，本文首先利用基于边缘的背景减方法去除背景干扰；然后利用GVFSnake技术提取出目标轮廓，结合目标轮廓改进了传统的颜色直方图；最后基于该颜色直方图结合卡尔曼滤波器或粒子滤波器改进了传统的均值漂移算法。实验表明，该算法可以实现快速的非刚性目标跟踪，对目标的不
不规则运动和严重遮挡有很好的鲁棒性。

A Non-Rigid Object Tracking Algorithm Based on Improved Mean Shift

The mean shift algorithm is improved to deal with the poor performance of the current mean shift in tracking non-rigid objects with eomplex movements. First, distractions in the background are removed through background subtrac- tion based on edges. Then, GVF snake is used to extract the target contour, which is used to improve the color histogram. In the end, mean shift is improved combining with the Kalman filter or the particle filter based on the eolor histogram. The experimental results show the real-time performance in tracking non-rigid objects and the robustness to irregular movements and heavy occlusion.