Visual Tracking using Particle Swarm Optimization
  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. 
Rafid Siddiqui, Siamak Khatibi
cs.CV