Shadow Removal for Moving Vehicles in Traffic Videos Using MATLAB

This project implements shadow removal for moving vehicles in traffic videos using MATLAB, accompanied by comprehensive video optimization. For shadow detection and elimination, we can employ background subtraction algorithms combined with chromaticity analysis - using functions like vision.ForegroundDetector and implementing color space transformations (RGB to HSV) to identify and remove shadows based on intensity and chromatic properties. In the video optimization phase, image enhancement algorithms such as histogram equalization (using histeq or adapthisteq functions) for contrast enhancement and color correction techniques (like white balancing with imhistmatch) significantly improve video clarity and quality. Additionally, motion estimation and compensation techniques can be implemented using optical flow methods (vision.OpticalFlow or vision.BlockMatcher) to reduce motion blur and stabilization issues. For advanced optimization, reinforcement learning algorithms can be integrated through MATLAB's Reinforcement Learning Toolbox to automatically adjust video parameters like brightness, contrast, and saturation thresholds for optimal results. The implementation typically involves frame-by-frame processing using VideoReader and VideoWriter objects, with parallel computing capabilities (parfor loops) for efficient handling of large video files. Through this comprehensive MATLAB-based approach for shadow removal and video optimization, we achieve significantly improved video quality during moving vehicle scenarios, producing clearer and more stable video footage with reduced artifacts and enhanced visual analytics capabilities.