MATLAB Object Tracking: Single and Multiple Target Implementation in Video Streams

In this documentation, we will explore how to implement single and multiple object tracking in video streams using MATLAB. Throughout the process, we will examine different types of tracking technologies, their advantages and disadvantages, and methodologies for selecting the most suitable tracking approach for specific tasks. This will include detailed explanations of key algorithms such as Kalman filters for motion prediction, correlation filters for appearance modeling, and multi-object tracking frameworks like Global Nearest Neighbor (GNN) or Joint Probabilistic Data Association (JPDA). Additionally, we will investigate techniques for visualizing and analyzing tracking results using MATLAB's Computer Vision Toolbox functions, including vision.PointTracker for feature-based tracking and vision.HistogramBasedTracker for histogram-based methods. The implementation will cover critical steps such as background subtraction using vision.ForegroundDetector, object detection with vision.BlobAnalysis, and trajectory plotting through custom MATLAB visualization scripts. Ultimately, we will develop a functional tracking system capable of accurately detecting and tracking objects in video streams, providing valuable applications for both research and practical implementations. The system will incorporate performance evaluation metrics and real-time processing considerations using MATLAB's videoReader and videoWriter objects for efficient stream handling.