Online Weighted One-Class Ensemble for Feature Selection in Background/Foreground Separation

Background subtraction (BS) is one of the key steps for detecting moving objects in video surveillance applications. In the last few years, many BS methods have been developed to handle the different challenges met in video surveillance but the role and the relevance of the visual features used has been less investigated. In this paper, we present an Online Weighted Ensemble of One-Class SVMs (Support Vector Machines) able to select suitable features for each pixel to distinguish the foreground objects from the background. In addition, our proposal uses a mechanism to update the relative importance of each feature over time. Moreover, a heuristic approach is used to reduce the complexity of the background model maintenance while maintaining the robustness of the background model. Results on two datasets show the pertinence of the approach.