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Visual Analysis of Humans : Looking at People / edited by Thomas B. Moeslund, Adrian Hilton, Volker Krüger, Leonid Sigal
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink BücherPublisher: London : Springer London, 2011Description: Online-Ressource (XXI, 632p. 186 illus., 168 illus. in color, digital)ISBN:- 9780857299970
- 9781283350822
- 006.6
- 006.37
- 006.4/2 23
- TA1637-1638 TA1637-1638
- TK7882.B56
Contents:
Summary: Understanding human activity from video is one of the central problems in the field of computer vision, driven by a wide variety of applications in communications, entertainment, security, commerce, and athletics. This unique text/reference provides a coherent and comprehensive overview of all aspects of video analysis of humans. Broad in coverage and accessible in style, the text presents original perspectives collected from preeminent researchers gathered from across the world. In addition to presenting state-of-the-art research, the book reviews the historical origins of the different existing methods, and predicts future trends and challenges. Topics and features: With a Foreword by Professor Larry Davis of the University of Maryland Institute for Advanced Computer Studies, USA Contains contributions from an international selection of leading authorities in the field Includes an extensive glossary. Discusses the problems associated with detecting and tracking people through camera networks. Examines topics related to determining the time-varying 3D pose of a person from video. Investigates the representation and recognition of human actions. Reviews the most important applications of activity recognition, from biometrics and surveillance, to sports and driver assistance. With its all-encompassing scope, this essential reference will be of great value not only to graduate students in computer vision, but also to all researchers and professionals interested in systems for the visual analysis of humans. Dr. Thomas B. Moeslund is an Associate Professor in the Department of Architecture, Design & ¡Media Technology¡at Aalborg University, Denmark. Dr. Adrian Hilton is Professor and Head of the Visual Media Research Group at the University of Surrey, Guildford, UK. Dr. Volker Krüger is an Associate Professor and Head of the Computer Vision and Machine Intelligence Laboratory at Aalborg University. Dr. Leonid Sigal is a Research Scientist at Disney Research, Pittsburgh, PA USASummary: This unique text/reference provides a coherent and comprehensive overview of all aspects of video analysis of humans. Broad in coverage and accessible in style, the text presents original perspectives collected from preeminent researchers gathered from across the world. In addition to presenting state-of-the-art research, the book reviews the historical origins of the different existing methods, and predicts future trends and challenges. This title: features a Foreword by Professor Larry Davis; contains contributions from an international selection of leading authorities in the field; includesPPN: PPN: 1651053669Package identifier: Produktsigel: ZDB-2-SCS
Visual Analysis of Humans; Foreword; Preface; Contents; Contributors; Part I: Detection and Tracking; Chapter 1: Is There Anybody Out There?; 1.1 Detection; 1.1.1 Data Acquisition; 1.1.2 Pixel-Based Detection; 1.1.3 Object-Based Detection; 1.2 Tracking; 1.3 Future Trends in Detection and Tracking; References; Chapter 2: Beyond the Static Camera: Issues and Trends in Active Vision; 2.1 Introduction; 2.2 Active Camera Con?gurations; 2.2.1 The Autonomous Camera Approach; 2.2.2 The Master/Slave Approach; 2.2.3 The Active Camera Network Approach; 2.2.4 Environmental Reasoning
2.3 The Autonomous Camera: A Hands-on Experience2.3.1 Camera-World Model; 2.3.2 Estimation; 2.3.3 Control; 2.3.4 System Performance; 2.3.4.1 Simulated Data; 2.3.4.2 Live Cameras; 2.3.5 Closing Remarks; 2.4 Active Vision in Practice: A Case Study; 2.4.1 Practical Considerations; 2.4.2 HERMES Hardware Platform; 2.4.3 HERMES Software Platform; 2.5 Conclusions: Learning from the Past to Foresee the Future; References; Chapter 3: Figure-Ground Segmentation-Pixel-Based; 3.1 Introduction; 3.2 Challenges in Scene Modeling; Illumination changes:; Motion changes:; Structural changes:
3.3 Statistical Scene Modeling3.3.1 Parametric Background Models; 3.3.2 Nonparametric Background Models; 3.3.2.1 KDE-Background Practice and Other Nonparametric Models; 3.4 Moving Shadow Suppression; 3.5 Tradeoffs in Background Maintenance; Background update rate:; Selective vs. blind update:; 3.6 Background Subtraction from a Moving Camera; 3.7 Conclusion and Further Reading; References; Chapter 4: Figure-Ground Segmentation-Object-Based; 4.1 Introduction; 4.2 Challenges and Outline; 4.3 Object Detection Approaches; 4.3.1 Sliding-Window Object Detection
4.7 Conclusion References; Chapter 5: Face Detection; 5.1 Introduction; 5.2 Categorization of Existing Approaches; 5.3 Representative Detection Methods: AdaBoost and Bag-of-Words; 5.3.1 AdaBoost; 5.3.1.1 Integral Image Representation; 5.3.1.2 Feature Selection; 5.3.1.3 Focus of Attention; 5.3.1.4 Extension to Other Facial Variations; 5.3.2 Bag-of-Words; 5.3.2.1 Interest-Point Detection and Representation; 5.3.2.2 Dictionary Formation; 5.3.2.3 Image Representation and Classi?cation; 5.4 Context; 5.4.1 Detecting Faces Using Semantic Context; 5.4.1.1 Design of Detectors
Visual Analysis of Humans; Foreword; Preface; Contents; Contributors; Part I: Detection and Tracking; Chapter 1: Is There Anybody Out There?; 1.1 Detection; 1.1.1 Data Acquisition; 1.1.2 Pixel-Based Detection; 1.1.3 Object-Based Detection; 1.2 Tracking; 1.3 Future Trends in Detection and Tracking; References; Chapter 2: Beyond the Static Camera: Issues and Trends in Active Vision; 2.1 Introduction; 2.2 Active Camera Con?gurations; 2.2.1 The Autonomous Camera Approach; 2.2.2 The Master/Slave Approach; 2.2.3 The Active Camera Network Approach; 2.2.4 Environmental Reasoning
2.3 The Autonomous Camera: A Hands-on Experience2.3.1 Camera-World Model; 2.3.2 Estimation; 2.3.3 Control; 2.3.4 System Performance; 2.3.4.1 Simulated Data; 2.3.4.2 Live Cameras; 2.3.5 Closing Remarks; 2.4 Active Vision in Practice: A Case Study; 2.4.1 Practical Considerations; 2.4.2 HERMES Hardware Platform; 2.4.3 HERMES Software Platform; 2.5 Conclusions: Learning from the Past to Foresee the Future; References; Chapter 3: Figure-Ground Segmentation-Pixel-Based; 3.1 Introduction; 3.2 Challenges in Scene Modeling; Illumination changes:; Motion changes:; Structural changes:
3.3 Statistical Scene Modeling3.3.1 Parametric Background Models; 3.3.2 Nonparametric Background Models; 3.3.2.1 KDE-Background Practice and Other Nonparametric Models; 3.4 Moving Shadow Suppression; 3.5 Tradeoffs in Background Maintenance; Background update rate:; Selective vs. blind update:; 3.6 Background Subtraction from a Moving Camera; 3.7 Conclusion and Further Reading; References; Chapter 4: Figure-Ground Segmentation-Object-Based; 4.1 Introduction; 4.2 Challenges and Outline; 4.3 Object Detection Approaches; 4.3.1 Sliding-Window Object Detection
4.3.2 Holistic Detector Representations4.3.3 Part-Based Detectors; 4.3.4 Local Feature-Based Detectors; 4.3.5 Use for Tracking-by-Detection; 4.4 Figure-Ground Segmentation; 4.4.1 Bounding Box Priors; 4.4.2 Bottom-Up Segmentation Re?nement; 4.4.3 Class-Speci?c Top-Down Segmentation; 4.4.4 Combinations; 4.5 Object Propagation; 4.5.1 Appearance-Based Tracking; 4.5.2 Online Classi?cation; 4.6 Applications; 4.6.1 Tracking-by-Detection from a Moving Platform; 4.6.2 Tracking Using the Continuous Detector Con?dence; 4.6.3 Tracking-Learning-Detection; 4.6.4 Articulated Multi-person Tracking
4.7 ConclusionReferences; Chapter 5: Face Detection; 5.1 Introduction; 5.2 Categorization of Existing Approaches; 5.3 Representative Detection Methods: AdaBoost and Bag-of-Words; 5.3.1 AdaBoost; 5.3.1.1 Integral Image Representation; 5.3.1.2 Feature Selection; 5.3.1.3 Focus of Attention; 5.3.1.4 Extension to Other Facial Variations; 5.3.2 Bag-of-Words; 5.3.2.1 Interest-Point Detection and Representation; 5.3.2.2 Dictionary Formation; 5.3.2.3 Image Representation and Classi?cation; 5.4 Context; 5.4.1 Detecting Faces Using Semantic Context; 5.4.1.1 Design of Detectors
5.4.1.2 Inference Through First-Order Logic [43]
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