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Record Nr. |
UNINA9910163995003321 |
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Autore |
Rezaei Mahdi |
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Titolo |
Computer Vision for Driver Assistance : Simultaneous Traffic and Driver Monitoring / / by Mahdi Rezaei, Reinhard Klette |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XIV, 224 p. 139 illus., 137 illus. in color.) |
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Collana |
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Computational Imaging and Vision, , 1381-6446 ; ; 45 |
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Disciplina |
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Soggetti |
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Computer science—Mathematics |
Computer mathematics |
Optical data processing |
Pattern recognition |
Mathematical Applications in Computer Science |
Image Processing and Computer Vision |
Pattern Recognition |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Vision-Based Driver-Assistance Systems -- Driver-Environment Understanding -- Computer Vision Basics -- Object Detection, Classification, and Tracking -- Driver Drowsiness Detection -- Driver Inattention Detection -- Vehicle Detection and Distance Estimation -- Fuzzy Fusion for Collision Avoidance. |
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Sommario/riassunto |
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This book summarises the state of the art in computer vision-based driver and road monitoring, focussing on monocular vision technology in particular, with the aim to address challenges of driver assistance and autonomous driving systems. While the systems designed for the assistance of drivers of on-road vehicles are currently converging to the design of autonomous vehicles, the research presented here focuses on scenarios where a driver is still assumed to pay attention to the traffic while operating a partially automated vehicle. Proposing various computer vision algorithms, techniques and methodologies, the authors also provide a general review of computer vision technologies that are relevant for driver assistance and fully autonomous vehicles. |
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Computer Vision for Driver Assistance is the first book of its kind and will appeal to undergraduate and graduate students, researchers, engineers and those generally interested in computer vision-related topics in modern vehicle design. . |
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