SFMOG : 초고속 MOG 기반 배경 제거 알고리즘

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참고문헌 (20)

1. Thierry Bouwmans, "Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey", Recent Patents on Computer Sciencee, 4(3), pp.147~176 (2011), Bentham Science Publishers Ltd.[DOI]
2. Andrews Sobral, "A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos", Computer Vision and Image Understanding, 122, pp.4~21 (2014), Elsevier BV[DOI]
3. Kamal Sehairi, "Comparative study of motion detection methods for video surveillance systems", Journal of Electronic Imaging, 26(2), pp.023025~ (2017), SPIE-Intl Soc Optical Eng[DOI]
4. Thierry Bouwmans, "Traditional and recent approaches in background modeling for foreground detection: An overview", Computer Science Review, 11-12, pp.31~66 (2014), Elsevier BV[DOI]
5. Belmar Garcia-Garcia, "Background subtraction in real applications: Challenges, current models and future directions", Computer Science Review, 35, pp.100204~ (2020), Elsevier BV[DOI]
6. Zoran Zivkovic, "Efficient adaptive density estimation per image pixel for the task of background subtraction", Pattern Recognition Letters, 27(7), pp.773~780 (2006), Elsevier BV[DOI]
7. Y. Wang, "CDnet 2014: An Expanded Change Detection Benchmark Dataset", IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.387~394 (2014)
8. C. Stauffer, "Adaptive background mixture models for real-time tracking", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2 (1999)
9. Thierry Bouwmans, "Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey", Recent Patents on Computer Sciencee, 1(3), pp.219~237 (2008), Bentham Science Publishers Ltd.[DOI]
10. S. Varadarajan, "Spatial mixture of Gaussians for dynamic background modelling", Advanced Video and Signal Based Surveillance (AVSS), 2013 10th IEEE International Conference, pp.63~68 (2013)
11. Sriram Varadarajan, "Region-based Mixture of Gaussians modelling for foreground detection in dynamic scenes", Pattern Recognition, 48(11), pp.3488~3503 (2015), Elsevier BV[DOI]
12. I. Martins, "BMOG: Boosted Gaussian Mixture Model with Controlled Complexity", Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2017), pp.50~57 (2017)
13. R. Wang, "Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models", IEEE Conference on Computer Vision and Pattern Recognition Workshops (2014)
14. I. Lissner, "Toward a Unified Color Space for Perception-Based Image Processing", IEEE Transactions on Image Processing, 21(3), pp.1153~1168 (2012), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
15. M. Balcilar, "Moving object detection usinglab2000hl color space with spatial and temporal smoothing", Applied Mathematics & Information Sciences, 8(4), pp.1755~1766 (2014)[DOI]
16. R. Cucchiara, "Detecting Moving Objects, Ghosts, and Shadows in Video Streams", IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), pp.1337~1342 (2003)
17. Simone Bianco, "Combination of Video Change Detection Algorithms by Genetic Programming", IEEE Transactions on Evolutionary Computation, 21(6), pp.914~928 (2017), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
18. Shengqin Jiang, "WeSamBE: A Weight-Sample-Based Method for Background Subtraction", IEEE Transactions on Circuits and Systems for Video Technology, 28(9), pp.2105~2115 (2018), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
19. P.-L. St-Charles, "A Self-Adjusting Approach to Change Detection Based on Background Word Consensus", IEEE Winter Conference on Applications of Computer Vision (WACV) (2015)
20. P. -L. St-Charles, "SuBSENSE: A Universal Change Detection Method with Local Adaptive Sensitivity", IEEE Transactions on Image Processing (2014)[DOI]

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