Spherical Object Tracking using Von Mises-Fisher Distribution in Catadioptric System

Tracking objects on video sequences is a very challenging task in computer vision applications. However, there is few articles that deal with this topic in catadioptric vision. This paper describes a new approach of omnidirectional images (gray level) processing based on inverse stereographic projection in the image plane. Our work is based on minimizing the distance between two models. The model named von Mises-Fisher distribution have as input the gabor phase and the measure used is Kullback–Leibler Divergence (KLD). In one hand, this model matching respect the deformed geometry of omnidirectional images due to using the spherical neighbourhood. In the other hand, the simulation results show that our approach gives as better performance in terms of overlapping estimation.