MATLAB-Based Average Frame Background Extraction Method with Algorithm Implementation

The MATLAB-based average frame background extraction method is a simple yet effective technique commonly used for background modeling in video processing. This method is particularly suitable for static or slowly changing scenes, enabling efficient separation of foreground and background elements. When processing in color spaces like RGB or HSV, it better preserves color information and improves the accuracy of background extraction.

The core algorithm involves constructing a background model by calculating the pixel-wise average across video frames. Key implementation steps include:

Frame Accumulation and Average Calculation: Read multiple video frames and compute the mean value for each pixel across all three RGB channels throughout the sequence. This approach eliminates transient foreground interferences (such as moving objects) to obtain a stable background model. In MATLAB implementation, this can be achieved using functions like read from VideoReader object and accumulating pixel values in a 3D matrix before applying mean() function along the temporal dimension.

Color Space Processing: Since RGB space is sensitive to illumination changes, images can be converted to alternative color spaces like HSV for computation. This reduces the impact of brightness variations on background extraction. MATLAB's rgb2hsv() function facilitates this conversion, allowing separate processing of hue, saturation, and value components.

Background Update (Optional): For scenes with slowly changing backgrounds, implement a sliding window approach to dynamically update the background model. This ensures adaptability in background extraction by maintaining a rolling average of recent frames using circular buffer techniques in MATLAB code.

Foreground Extraction: After obtaining the background model, calculate the difference between each new frame and the background model using metrics like Euclidean distance or absolute difference. Apply threshold segmentation using MATLAB's imbinarize() or custom thresholding functions to extract foreground objects. Morphological operations like imopen() and imclose() can refine the results.

This method offers high computational efficiency suitable for applications with moderate real-time requirements. However, for scenes with frequent dynamic changes (such as waving leaves or light flickering), more sophisticated background modeling approaches like Gaussian Mixture Models (GMM) using vision.ForegroundDetector or optical flow methods may be necessary for improved performance.