Fisheye lenses are renowned for their capacity to capture incredibly broad perspectives, often reaching up to 180 degrees. Their versatility extends beyond computer vision tasks and encompasses various fields. While applying computer vision techniques directly to fisheye images can yield suboptimal results, this article aims to introduce an uncomplicated and straightforward adaptation of the well-known Bayesian based particle filter. Through a few minor adjustments, we will demonstrate the potential to enhance the particle filter’s performance when dealing with such images, particularly for tracking purposes. In this study, we investigate the applicability of particle filters based on features such as: color space, Local Binary Pattern, Histogram of Oriented Gradients, and their combinations. Eventually, experiments and evaluations were carried out on hand annotated real videos with a top view fisheye camera in motion.