Abstract :
We propose a statistical motion model for sequential Bayesian tracking and show an adaptive particle filter algorithm for the motion model. It predicts the current state with the help of optical flows, i.e., it explores the state space with information based on the current and previous images of an image sequence. In addition, we introduce a robust method for state estimation and an automatic method for adjusting the variance of the motion model, which parameter is manually determined in most particle filters. In experiments with a real image sequence, we compare the proposed motion model with a random walk model, which is a widely used model for tracking, and show the proposed model outperform the random walk model.
