Dynamic Object Recognition, Tracking, and Detection in Videos

This project implements algorithms in MATLAB for recognizing, tracking, and detecting dynamic objects in video sequences. The implementation leverages computer vision and image processing techniques combined with machine learning algorithms to achieve precise object recognition and robust tracking capabilities. Key approaches may include background subtraction methods like Gaussian Mixture Models (GMM) for motion detection, optical flow techniques for tracking movement patterns, and classifier-based recognition using features like HOG (Histogram of Oriented Gradients) or deep learning architectures. The system can utilize MATLAB's Computer Vision Toolbox functions such as vision.ForegroundDetector for segmentation, vision.KalmanFilter for object tracking, and trainCascadeObjectDetector for recognition tasks. This implementation enhances video processing accuracy and efficiency, providing valuable data for subsequent applications and analysis. MATLAB demonstrates extensive applicability and potential in video processing and object recognition domains through its comprehensive toolkit and algorithm integration capabilities.