Video Object Tracking in MATLAB Using Frame-by-Frame Analysis

This MATLAB code implements a comprehensive video object tracking system through frame-by-frame analysis. The algorithm processes each video frame sequentially, employing computer vision techniques to detect and track moving objects. The core functionality involves extracting foreground objects using background subtraction methods like MOG2 or GMG, applying adaptive thresholding to create binary masks, and performing blob analysis using regionprops function to identify connected components.

The tracking mechanism utilizes centroid-based correlation between consecutive frames, calculating displacement vectors to estimate motion trajectories. Key MATLAB functions include VideoReader for frame extraction, vision.ForegroundDetector for background subtraction, and vision.BlobAnalysis for object characterization. The system implements Kalman filtering for predictive tracking and handles occlusion scenarios through trajectory extrapolation.

The modular architecture allows customization for various applications: vehicle tracking using morphological operations for noise reduction, wildlife monitoring with adjusted sensitivity parameters, and crowd analysis through multi-object tracking capabilities. Users can modify detection thresholds, region-of-interest constraints, and tracking persistence parameters to adapt to specific scenarios. The code includes visualization tools using insertShape and insertText functions to annotate tracked objects with bounding boxes and trajectory paths.