Background Subtraction Based on Random Superpixels Under Multiple Scales for Video Analytics

Background subtraction is a fundamental problem of computer vision, which is usually the first step of video analytics to extract the interesting region. Most previously available region-based background subtraction methods ignore the similarity between the pixels, meaning that the information gained from the pixels that do not contribute, or even contribute negatively to understanding an image, is taken into account. A new background subtraction model based on random superpixel segmentation under multiple scales is proposed. A custom region segmentation area is replaced with a superpixel segmentation area that uses similarity characteristics for pixels in the superpixel area. The compactness of the pixels in the same superpixel area means that the pixels positively contribute to understanding an image compared with when using custom region pixels. Superpixel segmentation is performed using the random simple linear iterative cluster method. Taking random samples during the superpixel segmentation process produces the Matthew effect, thus improving the robustness and efficiency of the model. Multi-scale superpixel segmentation is therefore guaranteed to give more accurate results. Standard benchmark experiments using the proposed approach produced encouraging results compared with the results given by previously available algorithms.