The aim of this paper is presentation of a vision based approach to tracking of human motion for the study of athletic performance. The proposed method is developed to capture markers positions associated with human motion obtained from video data. A datadriven predictor is used for tracking of wearing reflective markers. The uncertainty and occlusion of detected markers increase the noise in captured positions. So, a memory based data-driven system is proposed for solving of occlusion problems. The proposed system contains maximum a posteriori (MAP) estimator is jointed into loop predictor for reducing of noisy positions. The convergence behavior and tracking capability of proposed algorithm are proved. The result of tracking algorithm is tested over 12 sport men in 6 velocities over more than 30000 captured frames. Simulation results validate the analysis and ensuing method.