Tracking a firefly -a stable likelihood estimation for variable appearance object tracking-

The particle filter estimates a probability distribution of target objectpsilas state by sampled hypotheses and their weights. This method is more expressive than existing method such as Kalman filtering, because the object state is represented as a multi-modal distribution. However, the method canpsilat be directly applied to temporally variable appearance object tracking, for example, a firefly or a flickering neon-sign. For solving this problem, we propose a particle filter for a variable appearance object, which estimates a unique state parameter independent of targetpsilas position. Our method decomposes the state space into disjoint parameter spaces, i.e., object position and posture space and Appearance parameter space. In the appearance parameter space, the likelihood of each hypothesis is evaluated at the position parameters generated in the other space, and the best explain parameter is determined. Based on this parameter, likelihood in the position and posture space is evaluated. By interacting the parameter estimations in different spaces, we can successfully track blinking firefly in the darkness.