Real-time object tracking using color-based Kalman particle filter

Robust real-time tracking 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 method for real-time tracking of moving objects which is characterized by a color probability distribution is presented. We applied Kaiman particle filter (KPF) to color-based tracking. This KPF is a particle filter including the principle of Kalman filter. We modified this KPF for color-based tracking. This modified KPF can approximate the probabilistic density of the position of the tracked object properly and needs fewer particles for tracking than conventional particle filters and previous Kalman particle filter methods. We made experiments to confirm effectiveness of this method.