Adaptive Motion Model for Human Tracking Using Particle Filter

This paper presents a novel approach to model the complex motion of human using a probabilistic autoregressive moving average model. The parameters of the model are adaptively tuned during the course of tracking by utilizing the main varying components of the pdf of the target's acceleration and velocity. This motion model, along with the color histogram as the measurement model, has been incorporated in the particle filtering framework for human tracking. The proposed method is evaluated by PETS benchmark in which the targets have non-smooth motion and suddenly change their motion direction. Our method competes with the state-of-the-art techniques for human tracking in the real world scenario.