Motion Object Detection Algorithm Based on Gaussian Model

This article presents a Gaussian model-based motion object detection algorithm designed for real-time video analysis from surveillance cameras. The implementation utilizes YUV color space for computation and analysis, which offers advantages such as better human visual system adaptation and compression efficiency. The algorithm employs probabilistic background modeling where each pixel is represented as a mixture of Gaussian distributions, allowing robust handling of multimodal backgrounds and lighting variations. Key implementation steps include: 1. Converting input frames from RGB to YUV color space using color transformation matrices 2. Maintaining and updating Gaussian parameters (mean and variance) for each pixel over time 3. Classifying pixels as foreground or background based on Mahalanobis distance thresholding 4. Applying morphological operations to reduce noise and fill detection gaps This algorithm can be integrated into various applications including security surveillance systems and traffic monitoring solutions. Through this approach, we achieve efficient and accurate detection of moving objects while precisely identifying their positions and sizes within video streams, thereby enhancing the precision and reliability of monitoring and control processes.