Object tracking based on parzen particle filter using multiple cues

Abstract

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 multi-scale, 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. © Springer-Verlag Berlin Heidelberg 2007.