Superpixel-based online wagging one-class ensemble for feature selection in foreground/background separation

In the last decades, researchers in the field of Background Subtraction (BS) have developed methods to handle the different type of challenges. However, at the present time, no traditional algorithm seems to be able to simultaneously address all the key BS challenges. This can mainly be attributed to the lack of systematic investigation concerning the role and the importance of features within background modeling and foreground detection. In this paper, we present a novel online one-class ensemble based on wagging to select suitable features to each region of a certain scene to distinguish the foreground objects from the background. In addition, we propose a mechanism to update the importance of each feature discarding insignificantly features over time. The experimental results on three challenging datasets (i.e MSVS, RGB-D object detection, CD.net 2014) show the pertinence of the proposed approach.