Object Tracking Based on Parzen Particle Filter Using Multiple Cues

Particle filtering provides a general framework for propagating probability density functions in non-linear and non-Gaussian systems. However, generic particle filter (GPF) is based on Monte Carlo approach and sampling is a problematic issue. This paper introduces a parzen particle filter (PPF) which uses a general kernel approach to better approximate the posterior distribution rather than Dirac delta kernel in GPF. Furthermore, we adopt multiple cues and combine texture described by directional energy from multiscale, multi-orientation steerable filtering with color to characterize our tracking targets. The advantages of tracking with multiple cues compared to individual ones are demonstrated over experiments on artificial and natural sequences.