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In Computer Vision,object tracking is a very old and complex problem.Thoughthere are several existing algorithms for object tracking, still there areseveral challenges remain to be solved. For instance, variation of illuminationof light, noise, occlusion, sudden start and stop of moving object, shadingetc,make the object tracking a complex problem not only for dynamic backgroundbut also for static background. In this paper we propose a dual approach forobject tracking based on optical flow and swarm Intelligence.The optical flowbased KLT(Kanade-Lucas-Tomasi) tracker, tracks the dominant points of thetarget object from first frame to last frame of a video sequence;whereas swarmIntelligence based PSO (Particle Swarm Optimization) tracker simultaneouslytracks the boundary information of the target object from second frame to lastframe of the same video sequence.This dual function of tracking makes thetrackers very much robust with respect to the above stated problems. Theflexibility of our approach is that it can be successfully applicable invariable background as well as static background.We compare the performance ofthe proposed dual tracking algorithm with several benchmark datasets and obtainvery competitive results in general and in most of the cases we obtainedsuperior results using dual tracking algorithm. We also compare the performanceof the proposed dual tracker with some existing PSO based algorithms fortracking and achieved better results.

Dual approach for object tracking based on optical flow and swarm intelligence