Analysis of different noise filtering techniques for object detection and tracking from video with varying illumination

In order to improve object tracking and detection in films with different lighting conditions, this paper provides a thorough review of noise filtering methods. Computer vision systems face difficulties due to the variety of illumination conditions found in real-world settings, which calls for effective noise reduction techniques. We explore and evaluate several filtering techniques, such as deep learning-based methods, adaptive algorithms, guided bilateral filter and spatial and temporal filters, to determine how well they preserve object characteristics while cutting down on noise. We simulate real-world conditions with our studies on video datasets that have dynamic illumination changes. The outcomes show how well guided bilateral filter methods work in difficult illumination situations to preserve precise object trajectories and boundaries. To help practitioners choose the best noise filtering algorithms based on application-specific needs, we also go over the computational consequences and trade-offs related to each strategy. This work addresses a crucial component of real-world object detection and tracking in movies with variable lighting, offering insightful new information to the field of computer vision.