Visual Tracking using Particle Swarm Optimization

TL;DR

This paper introduces a robust visual tracking method using particle swarm optimization for non-linear image alignment, effective in diverse conditions, and validated on real and synthetic data.

Contribution

It presents a novel bio-metaheuristic approach for planar template tracking that improves robustness and accuracy over existing methods.

Findings

Successfully tracks planar regions under various transformations

Resilient to intensity variations in images

Outperforms some state-of-the-art methods in robustness

Abstract

The problem of robust extraction of visual odometry from a sequence of images obtained by an eye in hand camera configuration is addressed. A novel approach toward solving planar template based tracking is proposed which performs a non-linear image alignment for successful retrieval of camera transformations. In order to obtain global optimum a bio-metaheuristic is used for optimization of similarity among the planar regions. The proposed method is validated on image sequences with real as well as synthetic transformations and found to be resilient to intensity variations. A comparative analysis of the various similarity measures as well as various state-of-art methods reveal that the algorithm succeeds in tracking the planar regions robustly and has good potential to be used in real applications.