Visual Tracking using Particle Swarm Optimization
This addresses the problem of accurate camera transformation retrieval for applications like robotics or augmented reality, but it appears incremental as it builds on existing planar tracking methods with a bio-metaheuristic optimization.
The paper tackles robust visual odometry extraction from image sequences by proposing a novel planar template tracking method using non-linear image alignment and particle swarm optimization for global optimization, achieving resilience to intensity variations and robust tracking performance in comparative analysis.
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.