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Tracking of human sperm cells is a challenging task in computer vision due to the motion uncertainty. In this paper, we propose an efficient and effective algorithm for sperm cells tracking which attempts to capture the motion uncertainty of the target object. The tracking problem is formulated within the Bayesian filter framework. To address this problem, we incorporate an orientation adaptive mean shift optimization into particle filter framework. The proposed tracking algorithm significantly improves the sampling efficiency during the tracking process. We provide quantitative evaluations of the proposed method against existing tracking algorithms, and the experimental results demonstrate that our approach efficiently samples the object state, with accurate and robust tracking output for human sperm cells.