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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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 34; no. 5; pp. 1017 - 1023
Main Authors: Bohyung Han, Davis, L. S.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.05.2012
IEEE Computer Society
Subjects:
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
Online Access:Get full text
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Description
Summary: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.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2011.243