Adaptive Particle Filter based on the Kurtosis of Distribution

Abstract

Kurtosis based adaptive particle filter is presented in this paper. The concept of belief is proposed to each particle sampling and the distribution of particles can be adaptively changed according to the belief and motion information so that particles could track object in higher accuracy. The belief and motion information could be defined as a distance function of observation vector. In order to achieve this goal, we change the way of normal re-sampling technique. We introduce a framework that particles are re-sampled based on the distance function. We demonstrate the advantages of proposed method in two steps. First, we did strict simulation tests in 1D, 2D and 3D spaces to show that our method can give better result. Furthermore, we did the experiments in the real cases. One is real particle tracking in the hydraulic engineering area and the other is normal face tracking based on the color feature. We compared the result in each step to the result obtained from standard particle filter.