基于图割理论的尺度自适应人脸跟踪算法

摘要

Aiming at the problem of the excessive size-changing while the tracking window is enlarged by traditional Continuously Adaptive MeanShift (Camshift) algorithm in face tracking,an adaptive window face tracking method for Camshift based on graph cuts theory was proposed.Firstly,a graph cut area was created according to the Camshift iteration result of every frame by using graph cuts theory,and the skin lump was found by using Gaussian mixture model as weights of graph cuts.As a result,the tracking window could be updated by the skin lump.Then the real size of the target was obtained by computing the size of skin lump,and whether the target needed to be re-tracked was determined by comparing the size of the skin lump in the tracking window with that in the previous frame.Finally,the skin lump in last frame was used as the tracking target of the next frame.The experimental results demonstrate that the proposed method based on graph cuts can avoid interference of other skin color targets in the background,which effectively reflects the real face size-changing of the human body in rapid movement,and prevents the Camshift algorithm from losing the tracking target and falling into the local optimal solution with good usability and robustness.%针对连续自适应的Mean-Shift(Camshift)算法跟踪人脸时尺度过度放缩这一问题,提出了一种基于图割的Camshift人脸跟踪算法.首先,在每一帧图像的Camshift迭代结果内建立图割区域,使用高斯肤色模型作为图割权值分割出图割区域内肤色团块;然后,计算该肤色团大小得到目标真实尺度,并比较与上一帧图像跟踪框内肤色团的尺度来判断是否需要重新跟踪目标;最后,再以该团块作为下一帧跟踪目标.实验结果表明,基于图割的Camshift人脸跟踪算法有效地克服了跟踪时其他肤色区域的干扰,能有效地反映人体快速运动中人脸真实尺度变化,同时防止Camshift算法丢失跟踪目标而陷入局部最优解,具有较好的可用性和鲁棒性.