Adaptive probabilistic tracking with reliable particle selection

Abstract

A novel, effective probabilistic tracking method is proposed to adaptively capture the varying target appearance in a complex environment. Different from the traditional particle filter algorithms, the proposed method estimates the weight of each particle not only through similarity measurement between the target model and each hypothetical observation, but also through dissimilarity measurement between the background model and each hypothetical observation. The reliable particles with high weights are then selected to estimate the target state, and the target model is evolved over time with a novel model update strategy. Comparison experimental results demonstrate the robust performance of the proposed algorithm under challenging conditions.