基于改进Snake模型的人体运动跟踪算法

Human motion tracking algorithm based on improved Snake model

提出一种改进Snake模型与光流估计相结合的人体运动自动实时跟踪算法。利用角点检测得到接近人体真实轮廓的初始轮廓,减少了迭代次数,降低了Snake模型收敛到局部极值的概率;同时针对Snake模型跟踪不够稳定、容易出现跟踪丢失问题,结合KLT光流法,选取当前帧所得到的轮廓点中的强特征点进行光流估计,将估计结果作为下一帧Snake的初始轮廓,有效地解决了这一难题。实验结果表明改进Snake模型可使初始轮廓形变到人体真实轮廓,同时实现了视频序列中自动、实时的人体跟踪。

An automatic human motion tracking algorithm combining improved Snake model and optical flow was proposed. The initial contour most elose to human body was acquired by comer detection thus decreasing the iterations, and the probability of converging at local extreme value of Snake model was reduced. To resolve the problem that the tracking with Snake model is unstable and often loses the object, the strong feature points were chosen from the contour points in current frame for optical flow estimation, and then the result was chosen as the initial contour of next frame. Experimental results show that improved Snake model can make the initial coutour deform to the actual contour of human body, and realized the automatic and real-time human tracking.