Mean-shift algorithm integrating with SURF for tracking

A new algorithm is proposed to solve the issue of dynamically changing tracking window size in Mean-shift progress. Firstly, the algorithm detects feature points in the target area of current and previous frames using SURF. Epanechnikov kernel function is introduced to increase the weights of feature points in the central area. After matching feature points in two frames, we can calculate the target scale parameters which are used for adjusting the tracking window size in current frame and the bandwidth of kernel function. The algorithm is proved to have a good performance on real-time tracking using a moving camera.