Robust tracking of non-rigid objects is a challenging task. Particle filter is a powerful tool for vision tracking based on sequential Monte Carlo framework and proved very successful for non-linear and non-Gaussian estimation problem. This paper proposes a tracking algorithm based on particle filter and optimized Likelihood. Colour distributions are applied as they are robust to partial occlusion, rotation, scale invariant and computationally efficient. As the colour of an object can vary over time dependent on the illumination, the target model is adapted during temporally stable image observation. Particle filter approximates a posterior probability density of the state by using samples which are called particles. Here, the state is treated as the position of the object and the weight is considered as the likelihood of each particle. For this likelihood, we calculate the similarity between the colour histogram of the tracked object and the region around the position of each particle by using Bhattacharya distance. To enhance the results, a new parameter is multiplied by the previous likelihood to increase the particles weight. The system proves to be robust against problems of partial occlusion, full occlusion and illumination changes. Finally the mean state of the particles is treated as the estimated position of the object. The correctness as well as validity of the algorithm is demonstrated through the experiments results