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Background modeling and foreground detection for video surveillance /
Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and lo...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press/Taylor & Francis Group,
[2015]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Background modeling and foreground detection for video surveillance / |c edited by Thierry Bouwmans, Fatih Porikli, Benjamin Höferlin, Antoine Vacavant. |
| 264 | 1 | |a Boca Raton, FL : |b CRC Press/Taylor & Francis Group, |c [2015] | |
| 264 | 4 | |c ©20 | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource (xvix, 633 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 347 | |a data file | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Front Cover; Dedication; Contents; Preface; About the Editors; List of Contributors; I: Introduction and Background; 1. Traditional Approaches in Background Modeling for Static Cameras; 2. Recent Approaches in Background Modeling for Static Cameras; 3. Background Model Initialization for Static Cameras; 4. Background Subtraction for Moving Cameras; II: Traditional and Recent Models; 5. Statistical Models for Background Subtraction; 6. Non-parametric Background Segmentation with Feedback and Dynamic Controllers; 7. ViBe: A Disruptive Method for Background Subtraction. | |
| 505 | 8 | |a 8. Online Learning by Stochastic Approximation for Background Modeling9. Sparsity Driven Background Modeling and Foreground Detection; 10. Robust Detection of Moving Objects through Rough Set Theory Framework; III: Applications in Video Surveillance; 11. Background Learning with Support Vectors: Efficient Foreground Detection and Tracking for Automated Visual Surveillance; 12. Incremental Learning of an Infinite Beta-Liouville Mixture Model for Video Background Subtraction; 13. Spatio-temporal Background Models for Object Detection. | |
| 505 | 8 | |a 14. Background Modeling and Foreground Detection for Maritime Video Surveillance15. Hierarchical Scene Model for Spatial-color Mixture of Gaussians; 16. Online Robust Background Modeling via Alternating Grassmannian Optimization; IV: Sensors, Hardware and Implementations; 17. Ubiquitous Imaging (Light, Thermal, Range, Radar) Sensors for People Detection: An Overview; 18. RGB-D Cameras for Background-Foreground Segmentation; 19. Non-Parametric GPU Accelerated Background Modeling of Complex Scenes. | |
| 505 | 8 | |a 20. GPU Implementation for Background-Foreground-Separation via Robust PCA and Robust Subspace Tracking21. Background Subtraction on Embedded Hardware; 22. Resource-efficient Salient Foreground Detection for Embedded Smart Cameras; V: Benchmarking and Evaluation; 23. BGS Library: A Library Framework for Algorithms Evaluation in Foreground/Background Segmentation; 24. Overview and Benchmarking of Motion Detection Methods; 25. Evaluation of Background Models with Synthetic and Real Data; Color Insert. | |
| 520 | |a Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements. Incorporating both established and new ideas, Background Modeling and Foreground Detection for Video Surveillance provides a complete overview of the concepts, algorithms, and applications related to background modeling and foreground detection. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Video surveillance. | |
| 650 | 0 | |a Optical pattern recognition. | |
| 650 | 6 | |a Vidéosurveillance. | |
| 650 | 6 | |a Reconnaissance optique des formes (Informatique) | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Mechanical. |2 bisacsh | |
| 650 | 7 | |a Optical pattern recognition |2 fast | |
| 650 | 7 | |a Video surveillance |2 fast | |
| 700 | 1 | |a Bouwmans, Thierry, |e editor. | |
| 700 | 1 | |a Porikli, Fatih, |e editor. | |
| 700 | 1 | |a Höferlin, Benjamin, |e editor. | |
| 700 | 1 | |a Vacavant, Antoine, |e editor. | |
| 758 | |i has work: |a Background modeling and foreground detection for video surveillance (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFF3VtJDWkmKHYTXgqb36X |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |t Background modeling and foreground detection for video surveillance. |d Boca Raton, Florida : CRC Press, [2015] |z 9781482205374 |w (OCoLC)859584240 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1408079 |z Texto completo |
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