A New Framework for Background Subtraction Using Multiple Cues

Abstract

In this work, to effectively detect moving objects in a fixed camera scene, we propose a novel background subtraction framework employing diverse cues: pixel texture, pixel color and region appearance. The texture information of the scene is clustered by the conventional codebook based background modeling technique, and utilized to detect initial foreground regions. In this process, we employ a new texture operator namely, scene adaptive local binary pattern (SALBP) that provides more consistent and accurate texture-code generation by applying scene adaptive multiple thresholds. Background statistics of the color cues are also modeled by the codebook scheme and employed to refine the texture-based detection results by integrating color and texture characteristics. Finally, appearance of each refined foreground blob is verified by measuring the partial directed Hausdorff distance between the shape of a blob boundary and the edge-map of the corresponding sub-image region in the input frame. The proposed method is compared with other state-of-the-art background subtraction techniques and its results demonstrate that our method outperforms others for complicated environments in video surveillance applications.