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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Haque, M. Murshed, M. |
| Copyright Year | 2012 |
| Abstract | In this paper, we propose a new background subtraction technique based on perceptual mixture-of-Gaussians (PMOG). Unlike numerous variants of the classical MOG based approach [1], which can ensure reliable detection result only in known operating environments through proper parameter tuning, PMOG shows superior detection performance across dynamic unconstrained scenarios without any tuning. This is due to PMOG's intrinsic capability of exploiting several perceptual characteristics of human visual system for better understanding of the operating environment to avoid blind reliance on statistical observations. Furthermore, the proposed technique dynamically varies the model adaptation speed, i.e., learning rate, based on observed scene statistics for faster adaptation of changed background and better persistency of detected foreground entities. Comprehensive experimental evaluation on a number of standard datasets validates the robustness of the technique compared to the state-of-the-art. |
| Starting Page | 396 |
| Ending Page | 401 |
| File Size | 284088 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467320276 |
| DOI | 10.1109/ICMEW.2012.75 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-07-09 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Strontium Moving foreground detection Adaptation models PSNR Background subtraction Humans Dynamic background modelling Mixture of Gaussians (MOG) Gaussian mixture model (GMM) Positron emission tomography Standards |
| Content Type | Text |
| Resource Type | Article |
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