tracking object in video sequence is receiving enormous interest in computer vision research. This paper we contrast performance of Mean-Shift algorithm's gradient descent based search strategy with Kalman Filter based tracking algorithm used to models the dynamic motion of target object to guide optimize object's position through time using Swarm Intelligence based Particle Swarm Optimization. Experimental results of tracking a car demonstrate that the proposed Kalman Filter for object tracking is efficient under dynamic environment, robust in occlusion comes at the cost of higher computational requirement, helps to separate object pixel from background pixel for fast moving object. And optimize time for all vehicles detected in video are calculated by Particle Swarm Optimization.

several optimization techniques are proposed in artificial intelligence. This paper we contrast performance of Swarm Intelligence based PSO search strategy to optimize the multiple objective functions. Experimental analysis also demonstrated the effect of the inertia weight for multiple objective functions in the algorithm. And optimize time for all particles are detected and calculated by Particle Swarm Optimization.

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