Optic flow estimation by support vector regression

In this paper, we describe an approach to estimate optic flow from an image sequence based on Support Vector Regression (SVR) machines with an adaptive ɛɛ-margin. This approach uses affine and constant models for velocity vectors. Synthetic and real image sequences are used in order to compare results of the SVR approach against other well-known optic flow estimation methods. Experimental results on real traffic sequences show that SVR approach is an appropriate solution for object tracking.