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Green Image
Issue No. 05 - May (2012 vol. 34)
ISSN: 0162-8828
pp: 1017-1023
Bohyung Han , POSTECH (Pohang University of Science and Technology), Pohang
Larry S. Davis , University of Maryland, College Park
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.
Background modeling and subtraction, Haar-like features, support vector machine, kernel density approximation.

L. S. Davis and B. Han, "Density-Based Multifeature Background Subtraction with Support Vector Machine," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1017-1023, 2011.
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