Pedestrian Detection Algorithm in MATLAB

The MATLAB pedestrian detection algorithm is an image processing-based technology designed to identify pedestrians in images or video streams. This algorithm implementation involves multiple processing stages: image enhancement, background modeling, pedestrian detection, and tracking. Image enhancement techniques, such as histogram equalization or contrast adjustment using MATLAB's imadjust function, improve image quality to facilitate better pedestrian detection. Background modeling, typically achieved through methods like Gaussian Mixture Models (GMM) or foreground detection using vision.ForegroundDetector, helps the algorithm distinguish moving objects from static background elements. The core pedestrian detection component employs techniques like template matching using normxcorr2 for pattern recognition or feature extraction methods including HOG (Histogram of Oriented Gradients) features with trainCascadeObjectDetector. Finally, tracking algorithms utilizing Kalman filters or vision.PointTracker maintain pedestrian trajectories across consecutive frames, enabling better understanding of movement patterns and behavior analysis through frame-to-frame correlation.