Colour can provide effective graphic features for tracking nonrigid objects in real-time. However, the color of an object can vary over time dependent on the illumination, the visual angle to handle these appearance change a color based target model must be adapted during temporally stable image observation. The proposed method of this dissertation gives new observation likelihood model with dynamic parameter setting. Experiments show our proposed method is more accurate and more efficient than the traditional color histogram based particle filter. Integration of color distribution into particle filters and shows how these distributions can be adopted over time. A particle filter tracks several hypotheses simultaneously and weights them according to their similarity to the target model. As similarity measures between two color distributions, the popular Bhattacharyya coefficient is applied. In order to update the target model to slowly varying image conditions, Frames, where the object is occluded or too noisy, must be discarded.