Online Weighted One-Class Ensemble for Feature Selection in Background/Foreground Separation
Background subtraction (BS) is one of the key elements for detecting moving objects in video surveillance applications. In the last few years, researchers have been worked to develop BS methods to handle the different type of challenges in BS. However, at the present time, the role and the relevance of the visual features in BS has been less investigated. In this work, we present an Online Weighted Ensemble of One-Class SVMs able to select suitable features for each pixel to distinguish the foreground objects from the background. In addition, our framework uses a mechanism to update these importances features over time. Moreover, a efficient heuristic approach is used to background model maintenance. Experimental results on multispectral video sequences are shown to demonstrate the potential of the proposed approach.
* An incremental version of the WOC algorithm, called IncrementalWeighted One-Class Support Vector Machine (IWOC-SVM).
* An online weighted version of random subspace (OWRS) to increase the diversity of classifiers pool.
* A mechanism called Adaptive Importance Calculation (AIC) to suitably update the relative importance of each feature over time.
* A heuristic approach for IWOC-SVM model updating to improve speed.
ONLINE WEIGHTED ONE-CLASS ENSEMBLE FOR FEATURE SELECTION
A. Generate multiple weak models
B. Adaptive Importance (AI)
C. Background Detection
THE MOST (+) AND LESS (-) SIGNIFICANT FEATURES FROM MSVS SCENES.
PERFORMANCE OF THE DIFFERENT METHODS USING THE MSVS DATASET.
PUBLICATION AND SOURCE CODE
2016 - Silva, Caroline; Bouwmans, Thierry; Frélicot, Carl. "Online Weighted One-Class Ensemble for Feature Selection in Background/Foreground Separation". The International Conference on Pattern Recognition (ICPR), Cancun, Mexico (oral presentation), December, 2016. [PDF] [CODE]
2016 - Bouwmans, T. and Silva, C. and Marghes, C. and Zitouni, S. and Bhaskar, H. and Frélicot, C. “On the Role and the Importance of Features for Background Modeling and Foreground Detection”. Computer Science Review, 2016. [PDF]