【摘要】 针对传统Mean Shift中跟踪窗口尺度不能实时适应跟踪目标变化这一问题,提出一种基于图割理论的Mean Shift尺度自适应算法.根据每一帧图像的Mean Shift迭代结果,在其周围的一个小区域内,利用先验的肤色混合高斯模型构造图并建立关于标号的能量模型,使用max flow/min cut算法计算出能量函数最小值实现图割,在图割后的肤色团块中寻找最大团判定为跟踪目标,并以该团的尺度来实时调整目标跟踪窗口.实验结果表明,该方法克服了缩放10%核带宽的经典尺度适应方法的带宽趋于缩小问题,实时地反映跟踪目标真实尺度变化,避免背景中其他目标的干扰,具有较好的实用性和鲁棒性,而且可以应用到娱乐游戏控制中,丰富人机交互操作方式.更多还原

【Abstract】 This paper presents an adaptive window object tracking method for Mean Shift based on graph cuts theory.It copes with the size-changing object during visual tracking while the traditional Mean Shift can’t change the scale of tracking window in real time.According to the Mean Shift iteration result of every frame,graph is created by using skin color Gaussian mixture model in a small area around it.Graph cut is implemented by calculating the minimum energy function based on max flow/min cut principle.And then the largest skin lump is found,which is accepted as tracking object in the result of graph cuts.As a result,tracking window size can be updated by the largest skin lump. Experimental results clearly demonstrate that the method can avoid the problem of nonstop shrinking bandwidth effectively which is brought by expanding and shrinking 10%of kernel function bandwidth.It reflects the real scale change of tracking target in real time,avoids the interference of other targets in the background,and has good usability and robustness.Besides it can be applied to controlling entertainment games that enriches operation mode of human computer interaction.更多还原