Machine Learning for Vision-Based Motion Analysis : Theory and Techniques
Language: English
Published by Springer, 2013
Series: Book 17 of 86 - Advances in Computer Vision and Pattern Recognition
- Softcover
- New
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Seller Inventory # 9781447126072
- Title
- Machine Learning for Vision-Based Motion Analysis : Theory and Techniques
- Author
- Matti Pietikäinen
- Publisher
- Springer
- Publication year
- 2013
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1447126076
- ISBN 13
- 9781447126072
- Item weight
- 587 grams
- Dimensions
- 235x155x21 mm
- Series
- Book 17 of 86: Advances in Computer Vision and Pattern Recognition
Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition.
Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions.
Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets.
Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
"Synopsis" may belong to another edition of this title.
From the Back Cover
Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition.
Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions.
Topics and features:
- Provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms
- Examines algorithms for clustering and segmentation, and manifold learning for dynamical models
- Describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction
- Discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy
- Explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data
- Investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets
Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
Dr. Liang Wang is a lecturer at the Department of Computer Science at the University of Bath, UK, and is also affiliated to the National Laboratory of Pattern Recognition in Beijing, China. Dr. Guoying Zhao is an adjunct professor at the Department of Electrical and Information Engineering at the University of Oulu, Finland. Dr. Li Cheng is a research scientist at the Agency for Science, Technology and Research (A*STAR), Singapore. Dr. Matti Pietikäinen is Professor of Information Technology at the Department of Electrical and Information Engineering at the University of Oulu, Finland.
"About the title" may belong to another edition of this title.
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