Density-Based Multifeature Background Subtraction with Support Vector Machine
SCIE
SCOPUS
- Title
- Density-Based Multifeature Background Subtraction with Support Vector Machine
- Authors
- Han, B; Larry S. Davis
- Date Issued
- 2012-05
- Publisher
- IEEE
- Abstract
- 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.
- Keywords
- Background modeling and subtraction; Haar-like features; support vector machine; kernel density approximation; REAL-TIME TRACKING; OBJECT DETECTION
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/16530
- DOI
- 10.1109/TPAMI.2011.243
- ISSN
- 0162-8828
- Article Type
- Article
- Citation
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 34, no. 5, page. 1017 - 1023, 2012-05
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.