TY - JOUR T1 - Density-Based Multifeature Background Subtraction with Support Vector Machine JF - Pattern Analysis and Machine Intelligence, IEEE Transactions on Y1 - 2012 A1 - Han,Bohyung A1 - Davis, Larry S. KW - algorithm;density-based KW - application;illumination KW - approximation;object KW - background KW - camera;support KW - change;kernel KW - Computer KW - density KW - detection;pixelwise KW - detection;support KW - extraction;image KW - feature;background KW - generative KW - Haar-like KW - likelihood KW - machine;Haar KW - machines;vectors; KW - modeling KW - multifeature KW - segmentation KW - segmentation;object KW - subtraction KW - technique;discriminative KW - technique;high-level KW - techniques;spatial KW - transforms;cameras;computer KW - variation;spatio-temporal KW - variation;static KW - vector KW - vector;binary KW - VISION KW - vision;feature AB - 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. VL - 34 SN - 0162-8828 CP - 5 M3 - 10.1109/TPAMI.2011.243 ER -