A robust and efficient visual target tracking algorithm using particle filtering is proposed. Particle filtering has been proven very successful in estimating non-Gaussian and non-linear problems. In this paper, particle filter with color feature estimated the target state with time. Color feature being scale and rotational invariant, have showed robustness to partial occlusion and computationally efficient. The performance is made more robust by choosing the different (YIQ) color scheme. Tracking has been performed by comparison of chrominance histograms of target and candidate positions (particles). The Color based particle filter tracking often leads to inaccurate results when light intensity changes during a video stream. Furthermore, background subtraction has been used for size estimation of target. The qualitative evaluation of proposed algorithm is performed on several real world videos. The experimental results demonstrated that the proposed algorithm can track the moving objects well under illumination changes, occlusion and moving background. © 2014 IEEE.