Vehicle tracking by integrating motion vector estimation with particle filter

In particle filter based vehicle tracking, the second order autoregression model and the fixed particle propagation radius are often used for particles sampling. This would produce certain errors and cause the particles to deviate gradually from the vehicle´s true location in tracking. To resolve this problem, a modified state transition equation is built. In this equation, the vehicle´s current location is estimated using the motion vector of its center block and the particle propagation radius is updated using kalman filter. Both improvements make the state transition equation more accurate. The experiment results show that the proposed method can decrease the particles´ deviation and track vehicles more accurately than the particle filter using the second order autoregression model and the fixed particle propagation radius.