Adaptive spatiotemporal background modelling
Adaptive spatiotemporal background modelling
- Author(s): Y. Wang ; Y. Liang ; L. Zhang ; Q. Pan
- DOI: 10.1049/iet-cvi.2010.0229
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- Author(s): Y. Wang 1 ; Y. Liang 2 ; L. Zhang 3 ; Q. Pan 2
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View affiliations
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Affiliations:
1: School of Automation, Hangzhou Dianzi University, Hangzhou, People's Republic of China
2: School of Automation, Northwestern Polytechnical University, Xi'an, People's Republic of China
3: Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong, People's Republic of China
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Affiliations:
1: School of Automation, Hangzhou Dianzi University, Hangzhou, People's Republic of China
- Source:
Volume 6, Issue 5,
September 2012,
p.
451 – 458
DOI: 10.1049/iet-cvi.2010.0229 , Print ISSN 1751-9632, Online ISSN 1751-9640
In this study, one adaptive spatiotemporal background modelling algorithm is proposed for robust and reliable moving object detection in dynamic scene. First, a modified adaptive Gaussian mixture model (GMM) is presented to describe the temporal distribution of each pixel, based on which the spatial distribution of background is constructed by using non-parametric density estimation. By fusing the temporal and spatial distribution model, a heuristic strategy is presented for background subtraction. To reduce the computational cost, a novel criterion for adaptively determining the components number of GMM and the integral image method for calculating the spatial distribution model are proposed. Several experiments show that the proposed method can effectively reduce false positives caused by sudden or gradual changes of the background, and maintains lower false negatives, compared with some representative algorithms.
Inspec keywords: feature extraction; object detection; image motion analysis; Gaussian processes
Other keywords:
Subjects: Other topics in statistics; Other topics in statistics; Computer vision and image processing techniques; Optical, image and video signal processing
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