@article {13065,
	title = {Density-Based Multifeature Background Subtraction with Support Vector Machine},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {34},
	year = {2012},
	month = {2012/05//},
	pages = {1017 - 1023},
	abstract = {Background modeling and subtraction is a natural technique for object detection in videos captured by a static camera, and also a critical preprocessing step in various high-level computer vision applications. However, there have not been many studies concerning useful features and binary segmentation algorithms for this problem. We propose a pixelwise background modeling and subtraction technique using multiple features, where generative and discriminative techniques are combined for classification. In our algorithm, color, gradient, and Haar-like features are integrated to handle spatio-temporal variations for each pixel. A pixelwise generative background model is obtained for each feature efficiently and effectively by Kernel Density Approximation (KDA). Background subtraction is performed in a discriminative manner using a Support Vector Machine (SVM) over background likelihood vectors for a set of features. The proposed algorithm is robust to shadow, illumination changes, spatial variations of background. We compare the performance of the algorithm with other density-based methods using several different feature combinations and modeling techniques, both quantitatively and qualitatively.},
	keywords = {algorithm;density-based, application;illumination, approximation;object, background, camera;support, change;kernel, Computer, density, detection;pixelwise, detection;support, extraction;image, feature;background, generative, Haar-like, likelihood, machine;Haar, machines;vectors;, modeling, multifeature, segmentation, segmentation;object, subtraction, technique;discriminative, technique;high-level, techniques;spatial, transforms;cameras;computer, variation;spatio-temporal, variation;static, vector, vector;binary, VISION, vision;feature},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2011.243},
	author = {Han,Bohyung and Davis, Larry S.}
}
