Robust object tracking based upon adaptive multi-cue integration for video surveillance

摘要

It was well argued in literature that integrating multi-cue increases accuracy and robustness of visual tracking. Although, multi-cues object tracking using singlemodal or multimodal was explored by some of researchers, it still remains an open challenge to fuse multi-cue from different modularity under dynamic environment conditions. The aim of this research paper is to introduce a novel multi-cue object tracking framework using particle filter. In particle filter framework, our approach integrates cues while evaluating each particle instead of primitive approach of deciding cues performance in current frame based upon either a few present particles or previous state particles. First, we model our multi-cue tracking framework using Shafer's model and multi-cue data is combined using Conjunctive combination rules. The partial and total conflict among cues at particle level is redistributed efficiently using Proportional Conflict Redistribution (PCR-5) rules. In proposed model, automatic suppression/boosting of particles along with online conflict resolving facilitate resampling process for efficiently handling of particle degeneracy. Most importantly, compared to other state-of-art trackers, our proposed algorithm can handle more efficiently various dynamic environments conditions such as partial or full occlusion, illumination changes, weather, and visibility. In this manuscript, we demonstrate our proposed adaptive multi-cue fusion model on challenging benchmark video and thermal sequences and compare tracking results of our tracker with state-of- art trackers.