Density-Based Multifeature Background Subtraction with Support Vector Machine

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 segmentati...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 34; číslo 5; s. 1017 - 1023
Hlavní autori:	Bohyung Han, Davis, L. S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Los Alamitos, CA IEEE 01.05.2012 IEEE Computer Society
Predmet:	Applied sciences Artificial intelligence Background modeling and subtraction Computational modeling Computer science; control theory; systems Convergence Data processing. List processing. Character string processing Density functional theory Exact sciences and technology Haar-like features Image color analysis Kernel kernel density approximation Memory organisation. Data processing Pattern recognition. Digital image processing. Computational geometry Software support vector machine Support vector machines Vectors Computer vision Gradient Segmentation Image processing Video signal Shadow Haar-like features Background modeling and subtraction Haar function Spatial variation Object recognition Modeling Search algorithm Luminance Subtraction support vector machine Scene analysis Object detection Vector support machine Illumination Generative model kernel density approximation
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0162-8828 1939-3539 2160-9292 1939-3539
DOI:	10.1109/TPAMI.2011.243