Density-Based Multifeature Background Subtraction with Support Vector Machine

TitleDensity-Based Multifeature Background Subtraction with Support Vector Machine
Publication TypeJournal Articles
Year of Publication2012
AuthorsHan B, Davis LS
JournalPattern Analysis and Machine Intelligence, IEEE Transactions on
Pagination1017 - 1023
Date Published2012/05//
ISBN Number0162-8828
Keywordsalgorithm;density-based, application;illumination, approximation;object, background, camera;support, change;kernel, Computer, density, detection;pixelwise, detection;support, extraction;image, feature;background, generative, Haar-like, likelihood, machine;Haar, machines;vectors;, modeling, multifeature, segmentation, segmentation;object, subtraction, technique;discriminative, technique;high-level, techniques;spatial, transforms;cameras;computer, variation;spatio-temporal, variation;static, vector, vector;binary, VISION, vision;feature

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.