Object tracking using color-based Kalman particle filters

Robust real-time tacking of non-rigid object is a challenging task. Particle filtering has proven very successful for non-linear and non-Gaussian estimation problems. In this paper, a new approach to tracking using color-based particle filers is introduced. The tracked object is characterized by a color probability distribution. The goal of the tracking is to find a B-spline 2D curve in the current image, such that the distribution of the interior region of the curve most closely matches the target model distribution. The Kalman particle algorithm is used to reduce the number of particles needed in tracking mid improve the tracking speed. Results of several experiments are shown to demonstrate the effectiveness of our method.