Abstract
Object tracking in computer vision, essential for detecting, locating, and predicting object movement in videos [5], faces challenges with fisheye cameras due to wide-angle views, distortion, and complex perspective shifts. Particle Filters offer a solution by providing a flexible framework for tracking objects in nonlinear, wide-angle scenes. They excel in handling fisheye image characteristics like distortion correction and probabilistic state estimation, making them valuable for robust object tracking in fisheye camera setups.