A spatiotemporal background extractor using a single-layer codebook model

Background subtraction is a crucial component in visual surveillance, which has been studied over years. However, an efficient algorithm that can tolerate the environment changes such as dynamic backgrounds and sudden changes of illumination is still demanding. In this paper, we design an innovative framework called the spatiotemporal background extractor (SBE) from a single-layer codebook model. Two main extractors, the background extractor (BE) and the background gradient extractor (BGE), are constructed to extract the foreground objects. The background extractor is built for each single frame with spatial information propagated from the neighbor locations, which is useful for handling dynamic background and sudden lighting changes. The background gradient extractor is also constructed and updated, and we design a propagation forbidden policy for background updating, so as to keep the completeness of foreground shape via the background gradient information. The proposed method can efficiently capture the foreground and eliminates the noise of background. The performance of the proposed method is compared with MoG [3], Codebook [4] and ViBe [8] on the Wallflower [1] and Perception [2] datasets.