Using and advancement of machine vision techniques in industrial, military and medical applications were outstanding in last two decades. It has been employed for determining some diseases by non-invasive methods. It is employed in order to determinate infertility in males. Different Diseases in sperms leads to deforming of their shapes. Deforming in shape of head, tail, neck of sperms causes different motion of sperms. In sequence, extracting sperms motion feature is used for Tracking of human sperm cells is a challenging task in computer vision due to the motion uncertainty. In this work, extracting trajectory of sperms for determining sperms disease has been presented. Particle Filter (PF) and Kalman Filters are two of the most reputable algorithms that used in object tracking. In order to finding optimum response of Kalman filter, constraints such as linear state model and Gaussian noise should be satisfied. Also, probability density function of state model variables should be Gaussian function. However, in most systems, these conditions are not met and other solutions, such as Monte Carlo method, are used. Monte Carlo has been proposed to solve problems associated with nonlinear non-Gaussian probability distribution and non-Gaussian noise. In this work, a new method of multiple sperm tracking based on Particle filtering and C-means clustering algorithms is considered which reduces sperms clutter effects in tracking. This algorithm is functioning in two step enhancement after removing the background and small areas successfully. Additionally, sperms are detected by comparing their color histogram using Bhattacharya distance method with the same features of a reference specimen. Finally, by executing a simple threshold, sperms have been detected. In tracking step, first, single sperm tracking is implemented. Estimated trajectory of kalman is compared with that of PF. Then, multiple sperm tracking was evaluated. In multiple object tracking, and in the case which objects are similar, problems such as clutter and occlusion are occurring. However, in multiple sperm tracking, occlusion would not be a problem. Two methods were considered in this section, which one is by not assuming clutter effect, and another one is by assuming this effect. In First case, proposed algorithm works well only when sperms are far apart. And as sperms become close together, this algorithm fails. In order to prevent this failure, a second method for reducing clutter effects has been presented, that would track sperms acceptably even if they become close together. This algorithm is based on clustering of particles of PF algorit Key Words Computer vision, fertility, Particle Filter, nonlinear model, multiple object tracking, clutter, C-means.