基于区域生长的Mean shift动态变形手势跟踪算法

针对传统Mean shift算法在手势跟踪中由于搜索窗口内手势背景像素改变所导致的跟踪精度不高,以及算法本身由于手势模型更新所引起的时间消耗较多等问题,提出一种基于区域生长与Mean shift算法相结合的动态变形手势跟踪算法。该算法在跟踪初始阶段通过帧间差分法对手势中心完成自动初始化,利用区域生长算法采集手势样本点,并通过Mean shift算法对目标中心进行精确定位。实验结果表明,该方法能够对动态变形手势实现精确实时的跟踪,可较好地降低算法的时间复杂度,保证运动目标跟踪的稳定性和连续性。

Mean Shift Dynamic Deforming Hand Gesture Tracking Algorithm Based on Region Growth

Considering the traditional Mean shift algorithm has the problem of low tracking accuracy owing to the background pixels changing in searching window and that of high time complexity led by the updating of hand gesture model, a dynamic deforming hand gesture tracking algorithm based on the integration of region growth and Mean shift is proposed in this paper. The initiation of hand gesture center is automatically accomplished by the frames difference method at the initial tracking stage. Then, the hand gesture pixel samples are gathered by the region growth method. Finally, the position of the object center is accurately located by the Mean shift. Experimental results show that the proposed method can track the dynamic deforming hand gestures accurately in real time, reduce the time complexity of algorithm and make certain the stability and the continuity of the dynamic object tracking.