Research on robust visual tracker based on multi-cue correlation particle filters

Abstract

For the problem of robust visual tracking in various complex tracking scenarios, a multi-cue correlation particle filter (CPF for short) visual tracker supervised by population convergence is proposed. By combining the advantages of particle filter and correlation filter, the CPF tracker gains better robustness, computational efficiency and stability for visual tracking. Meanwhile, to solve the problem of sample diversity in traditional CPF tracker, a genetic operating algorithm supervised by population convergence is proposed and introduced to the resampling process of CPF. Then considering that a single kind of feature weakens the tracking efficiency and robustness of our tracker, we propose to combine different types of features including Harris feature, HOG feature and SIFT feature based on fuzzy control theory to form a multi-cue CPF tracker (SPC-MCCPF for short). Multiple experiments on the OTB2015 and VOT2018 datasets prove that our tracker is quite effective in dealing with various challenging tracking problems.