Edge-based foreground detection with higher order derivative Local Binary Patterns for low-resolution video processing

Foreground segmentation is an important task in many computer vision applications and a commonly used approach to separate foreground objects from the background. Extremely low-resolution foreground segmentation, e.g. on video with resolution of 30×30 pixels, requires modifications of traditional high-resolution methods. In this paper, we adapt a texture-based foreground segmentation algorithm based on Local Binary Patterns (LBPs) into an edge-based method for low-resolution video processing. The edge information in the background model is introduced by a novel LBP strategy with higher order derivatives. Therefore, we propose two new LBP operators. Similar to the gradient operator and the Laplacian operator, the edge information is obtained by the magnitudes of First Order Derivative LBPs (FOD-LBPs) and the signs of Second Order Derivative LBPs (SOD-LBPs). Posterior to background subtraction, foreground corresponds to edges on moving objects. The method is implemented and tested on low-resolution images produced by monochromatic smart sensors. In the presence of illumination changes, the edge-based method outperforms texture-based foreground segmentation at low resolutions. In this work, we demonstrate that edge information becomes more relevant than texture information when the image resolution scales down.