Application of Particle Filtering in Visual Tracking

Abstract

Particle filtering, also known as Sequential Monte Carlo methods (SMC), is a sophisticated model estimation techniques based on simulation. Particle filtering has important applications in location, tracking and other fields. It indicates probability using particle set and can be used in any space-state model. Its core idea is to extract a random state from the posterior probability express the distribution. In general, particle filtering is a process which uses a set of stochastic sample propagating in space-state to approximate probability density function and to replace integral operation with mean value of a sample to obtain minimum state variance distribution. It solves the restriction that nonlinear filtering should match Gaussian distribution, expresses a wider range of distribution than Gaussian distribution and has a strong ability to model the nonlinear characteristic of variance parameter. This paper introduces the application of particle filtering in visual tracking. Finally, it puts forward some improved algorithms to revise the inherent deficiencies existing in particle filtering.