Image Change Detection Algorithm

In the original implementation, we employed an image change detection algorithm that generates change masks from difference images through t-test statistical analysis. The algorithm successfully identified moving objects in the image sequence. The core mechanism involves comparing pixel-level differences between sequential images to detect object motion. Through systematic image analysis and processing, the algorithm accurately identifies variations in the image data and annotates them as moving objects. This approach has broad applications in computer vision and image processing domains, providing robust solutions for motion analysis and change detection in visual data. Key implementation aspects include calculating difference images between frames, applying t-test thresholds to determine significant changes, and generating binary masks to highlight moving regions. The algorithm effectively handles illumination variations and noise through statistical significance testing, ensuring reliable detection performance.