Robust Object Tracking with Crow Search Optimized Multi-cue Particle Filter

TL;DR

This paper introduces a robust particle filter tracking method enhanced by Crow Search Optimization for outlier detection and an adaptive fuzzy fusion model for multi-cue integration, significantly improving tracking accuracy and convergence.

Contribution

It presents a novel resampling approach using Crow Search Optimization and an adaptive fuzzy fusion model for multi-cue data integration in particle filtering.

Findings

Outperforms state-of-the-art trackers on benchmark videos

Achieves an average CLE of 7.98 and F-measure of 0.734

Effectively handles various tracking challenges

Abstract

Particle Filter(PF) is used extensively for estimation of target Non-linear and Non-gaussian state. However, its performance suffers due to inherent problem of sample degeneracy and impoverishment. In order to address this, we propose a novel resampling method based upon Crow Search Optimization to overcome low performing particles detected as outlier. Proposed outlier detection mechanism with transductive reliability achieve faster convergence of proposed PF tracking framework. In addition, we present an adaptive fuzzy fusion model to integrate multi-cue extracted for each evaluated particle. Automatic boosting and suppression of particles using proposed fusion model not only enhances performance of resampling method but also achieve optimal state estimation. Performance of the proposed tracker is evaluated over 12 benchmark video sequences and compared with state-of-the-art solutions. Qualitative and quantitative results reveals that the proposed tracker not only outperforms existing solutions but also efficiently handle various tracking challenges. On average of outcome, we achieve CLE of 7.98 and F-measure of 0.734.