Background Subtraction Using Median Approximation Algorithm Implementation

This article explores a median approximation algorithm commonly used for background subtraction and provides a MATLAB implementation example. Background subtraction is a crucial technique in image processing and computer vision that enables the extraction of foreground objects from video sequences by separating them from the background environment. This method finds extensive applications in various domains including video surveillance systems, motion analysis frameworks, and pedestrian detection algorithms. The MATLAB implementation leverages the statistical robustness of median operations to create a background model that adapts to gradual lighting changes while remaining resistant to temporary foreground objects. The core algorithm works by maintaining a buffer of recent frames and computing the median value for each pixel position, which effectively represents the static background scene. Key MATLAB functions employed include matrix operations for efficient frame processing, statistical functions for median calculations, and morphological operations for noise reduction in the resulting foreground mask. Through detailed code analysis, this article provides insights into practical implementation considerations such as buffer size optimization, computational efficiency improvements, and threshold selection strategies for robust foreground detection.