Adaptive particle filter for robust visual tracking

Abstract

Object tracking plays a key role in the field of computer vision. Particle filter has been widely used for visual tracking under nonlinear and/or non-Gaussian circumstances. In particle filter, the state transition model for predicting the next location of tracked object assumes the object motion is invariable, which cannot well approximate the varying dynamics of the motion changes. In addition, the state estimate calculated by the mean of all the weighted particles is coarse or inaccurate due to various noise disturbances. Both these two factors may degrade tracking performance greatly. In this work, an adaptive particle filter (APF) with a velocity-updating based transition model (VTM) and an adaptive state estimate approach (ASEA) is proposed to improve object tracking. In APF, the motion velocity embedded into the state transition model is updated continuously by a recursive equation, and the state estimate is obtained adaptively according to the state posterior distribution. The experiment results show that the APF can increase the tracking accuracy and efficiency in complex environments.