  In Computer Vision,object tracking is a very old and complex problem.Though
there are several existing algorithms for object tracking, still there are
several challenges remain to be solved. For instance, variation of illumination
of light, noise, occlusion, sudden start and stop of moving object, shading
etc,make the object tracking a complex problem not only for dynamic background
but also for static background. In this paper we propose a dual approach for
object tracking based on optical flow and swarm Intelligence.The optical flow
based KLT(Kanade-Lucas-Tomasi) tracker, tracks the dominant points of the
target object from first frame to last frame of a video sequence;whereas swarm
Intelligence based PSO (Particle Swarm Optimization) tracker simultaneously
tracks the boundary information of the target object from second frame to last
frame of the same video sequence.This dual function of tracking makes the
trackers very much robust with respect to the above stated problems. The
flexibility of our approach is that it can be successfully applicable in
variable background as well as static background.We compare the performance of
the proposed dual tracking algorithm with several benchmark datasets and obtain
very competitive results in general and in most of the cases we obtained
superior results using dual tracking algorithm. We also compare the performance
of the proposed dual tracker with some existing PSO based algorithms for
tracking and achieved better results.
