A novel FAST-Snake object tracking approach

We present a novel FAST-Snake tracking approach using improved FAST-feature matching to estimate affine transform of contour points between frames as the initial contour of the Snake model. For real-time tracking, we define a prior constraint energy in the Snake model and adopt the greedy algorithm to implement contour optimization. Experiments involving 3-D object database and video sequences show that the proposed approach is superior to its counterpart in terms of mean square error (MSE) and convergence speed, and that it has the adaptability to complex motion and partial occlusion.