Learning particle filter for multiple target tracking

This paper presents a learning particle filter (LPF) to solve the problems of uncertainty, varying number, overlap, ambiguous, non-rigid, nonlinear, and non-Gaussian in tracking multiple visual targets. Evolution learning obtains a detector that guides proposal distribution originally, and then online learning renders the detector adaptive to pose altering and proposal distribution closer to posterior distribution. Simulation results demonstrate the performance of LPF algorithm.