MATLAB Implementation of Multi-Object Detection

Multi-object detection is a crucial task in computer vision and image processing, primarily applied in fields such as video surveillance, visual object tracking, and motion detection. Implementing multi-object detection in MATLAB typically involves the following key steps: Image or Video Input Processing: The process begins by reading video streams or image sequences. MATLAB provides powerful video processing and image reading functions (e.g., `VideoReader` for video input and `imread` for image sequences) that facilitate frame-by-frame analysis of input data. Foreground Detection and Background Modeling: Common approaches include background subtraction methods, such as Gaussian Mixture Models (GMM) implemented via `vision.ForegroundDetector` or optical flow-based techniques (using `opticalFlowHS` or `opticalFlowLK` functions) to separate moving objects from static backgrounds. Target Detection and Localization: After extracting the foreground, morphological operations (e.g., opening and closing using `imopen` and `imclose`) help remove noise. Connected component analysis with functions like `regionprops` then detects object positions and dimensions by calculating properties such as 'Area', 'Centroid', and 'BoundingBox'. Object Tracking: Building on multi-object detection, tracking algorithms like Kalman filtering (implemented with `vision.KalmanFilter`) or the Hungarian algorithm (via `assignDetectionsToTracks`) can be integrated to correctly associate the same target across different frames. Result Visualization: MATLAB offers comprehensive plotting and image display capabilities (e.g., `insertShape` to draw bounding boxes or `plot` for trajectory visualization), enabling real-time rendering of detection results for analysis and debugging. Multi-object detection finds extensive applications in intelligent surveillance, autonomous driving, and motion analysis. Leveraging MATLAB's robust toolboxes (such as the Computer Vision Toolbox and Image Processing Toolbox) and efficient computational performance, it serves as an ideal platform for rapid prototyping and validation.