There are several features that are used in the literature and they can be classified in the following categories:
Features in the pixel domain (360 papers)
Features in a transform domain (32 papers)
Features in deep learning domain (17 papers)
Features for background/foreground separation presents the following issues:
Choice of the features size: pixel, a block or a cluster.
Choice of the features type: color features, edge features, stereo features, motion features and texture features.
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As this website gives many information that come from my research, please cite my following papers:
C. Silva, T. Bouwmans, C. Frelicot, 'An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos", VISAPP 2015, Berlin, Germany, March 2015.
C. Silva, T. Bouwmans, C. Frelicot, "Online Weighted One-Class Ensemble for Feature Selection in Background/Foreground Separation", International Conference on Pattern Recognition, ICPR 2016, December 2016.
C. Pacheco, T. Bouwmans, C. Frelicot,"Superpixel-based online wagging one-class ensemble for feature selection in foreground/background separation", Pattern Recognition Letters, 2017.
T. Bouwmans, C. Silva, C. Marghes, M. Zitouni, H. Bhaskar, C. Frelicot, “On the Role and the Importance of Features for Background Modeling and Foreground Detection”, Computer Science Review, Volume 28, pages 26-91, May 2018.
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