Low-complexity background subtraction based on spatial similarity

Robust detection of moving objects from video sequences is an important task in machine vision systems and applications. To detect moving objects, accurate background subtraction is essential. In real environments, due to complex and various background types, background subtraction is a challenging task. In this paper, we propose a pixel-based background subtraction method based on spatial similarity. The main difficulties of background subtraction include various background changes, shadows, and objects similar in color to background areas. In order to address these problems, we first computed the spatial similarity using the structural similarity method (SSIM). Spatial similarity is an effective way of eliminating shadows and detecting objects similar to the background areas. With spatial similarity, we roughly eliminated most background pixels such as shadows and moving background areas, while preserving objects that are similar to the background regions. Finally, the remaining pixels were classified as background pixels and foreground pixels using density estimation. Previous methods based on density estimation required high computational complexity. However, by selecting the minimum number of features and deleting most background pixels, we were able to significantly reduce the level of computational complexity. We compared our method with some existing background modeling methods. The experimental results show that the proposed method produced more accurate and stable results.