%0 Journal Article %J Pattern Analysis and Machine Intelligence, IEEE Transactions on %D 2012 %T Density-Based Multifeature Background Subtraction with Support Vector Machine %A Han,Bohyung %A Davis, Larry S. %K algorithm;density-based %K application;illumination %K approximation;object %K background %K camera;support %K change;kernel %K Computer %K density %K detection;pixelwise %K detection;support %K extraction;image %K feature;background %K generative %K Haar-like %K likelihood %K machine;Haar %K machines;vectors; %K modeling %K multifeature %K segmentation %K segmentation;object %K subtraction %K technique;discriminative %K technique;high-level %K techniques;spatial %K transforms;cameras;computer %K variation;spatio-temporal %K variation;static %K vector %K vector;binary %K VISION %K vision;feature %X 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. %B Pattern Analysis and Machine Intelligence, IEEE Transactions on %V 34 %P 1017 - 1023 %8 2012/05// %@ 0162-8828 %G eng %N 5 %R 10.1109/TPAMI.2011.243