MATLAB Source Code for Moving Object Detection in Video Image Sequences

Moving object detection is a crucial application in computer vision, primarily used for identifying and segmenting moving objects from video sequences. Implementing these algorithms in MATLAB typically involves key techniques such as background modeling, frame differencing, and morphological processing.

Background Modeling Background modeling is one of the most commonly used methods, which detects moving objects by establishing a static background model of the scene and comparing current frames against this background. Common approaches include Gaussian Mixture Models (GMM) and adaptive background modeling. In MATLAB implementation, the Computer Vision Toolbox provides vision.ForegroundDetector which uses GMM, or developers can manually implement GMM using statistical functions for more customization.

Frame Differencing Frame differencing detects moving objects by comparing differences between consecutive two or three frames. This method is computationally simple but sensitive to noise, typically requiring post-processing to optimize results. The implementation involves using imabsdiff function to calculate absolute differences between frames and applying thresholding to identify significant changes.

Morphological Processing After initial detection of moving objects, morphological operations (such as opening and closing) are typically applied to remove noise and fill target regions, thereby improving detection accuracy. MATLAB's image processing toolbox provides functions like imopen, imclose, and bwareaopen for these operations, which help clean up binary masks obtained from detection algorithms.

MATLAB Implementation Approach Video Reading: Use VideoReader to load video files and process frame by frame, ensuring efficient memory management through iterative frame extraction. Background Modeling: Choose between vision.ForegroundDetector for quick implementation or manual GMM implementation using gmdistribution for customized background modeling. Motion Detection: Calculate inter-frame differences using diff operations or directly apply background subtraction methods with adaptive thresholding techniques. Post-processing: Apply morphological filtering using strel to create structuring elements, followed by connected component analysis using bwconncomp to optimize object segmentation results and remove false positives.

This methodology can be applied to various fields including surveillance systems and traffic analysis. Integration with more advanced algorithms like optical flow or deep learning-based approaches can further enhance detection performance and accuracy.