Particle filter on GPUs for real-time tracking

Abstract

Efficient object tracking is required by many Computer Vision application areas like surveillance or robotics. It deals with statespace variables estimation of interesting features in image sequences and their future prediction. Probabilistic algorithms has been widely applied to tracking. These methods take advantage of knowledge about previous states of the system reducing the computational cost of an exhaustive search over the whole image. In this framework, posterior probability density function (pdf) of the state is estimated in two stages: prediction and update. General particle filters are based on discrete representations of probability densities and can be applied to any state-space model [Arulampalam et al. 2002]. Discrete particles j of a set (Xt ,Πt) = {(x0 t ,π0 t )...(xN t ,πN t )} in time step t, contains information about one possible state of the system x j t and its importance weight π j t . In a practical approach, particle weights computation is the most expensive stage of the particle filter algorithm, and it has to be executed at each time step for every particle [Deutscher et al. 2000].