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Author Rezaei, Mahdi,
Title Computer vision for driver assistance : simultaneous traffic and driver monitoring / Mahdi Rezaei, Reinhard Klette.
Imprint Cham, Switzerland : Springer, 2017.

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Series Computational imaging and vision, 1381-6446 ; volume 45
Computational imaging and vision ; v. 45.
Subject Driver assistance systems.
Computer vision.
Alt Name Klette, Reinhard,
LOCATION CALL # STATUS MESSAGE
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Series Computational imaging and vision, 1381-6446 ; volume 45
Computational imaging and vision ; v. 45.
Subject Driver assistance systems.
Computer vision.
Alt Name Klette, Reinhard,
Description 1 online resource.
Contents Preface; Contents; Symbols; 1 Vision-Based Driver-Assistance Systems; 1.1 Driver-Assistance Towards Autonomous Driving; 1.2 Sensors; 1.3 Vision-Based Driver Assistance; 1.4 Safety and Comfort Functionalities; 1.5 VB-DAS Examples; 1.6 Current Developments; 1.7 Scope of the Book; 2 Driver-Environment Understanding; 2.1 Driver and Environment; 2.2 Driver Monitoring; 2.3 Basic Environment Monitoring; 2.4 Midlevel Environment Perception; 3 Computer Vision Basics; 3.1 Image Notations; 3.2 The Integral Image; 3.3 RGB to HSV Conversion; 3.4 Line Detection by Hough Transform; 3.5 Cameras.
3.6 Stereo Vision and Energy Optimization3.7 Stereo Matching; 4 Object Detection, Classification, and Tracking; 4.1 Object Detection and Classification; 4.2 Supervised Classification Techniques; 4.2.1 The Support Vector Machine; 4.2.2 The Histogram of Oriented Gradients; 4.2.3 Haar-Like Features; 4.3 Unsupervised Classification Techniques; 4.3.1 k-Means Clustering; 4.3.2 Gaussian Mixture Models; 4.4 Object Tracking; 4.4.1 Mean Shift ; 4.4.1.1 Mean Shift Tracking; 4.4.2 Continuously Adaptive Mean Shift; 4.4.3 The Kanade-Lucas-Tomasi (KLT) Tracker; 4.4.4 Kalman Filter.
4.4.4.1 Filter Implementation4.4.4.2 Tracking by Prediction and Refinement; 5 Driver Drowsiness Detection; 5.1 Introduction; 5.2 Training Phase: The Dataset; 5.3 Boosting Parameters; 5.4 Application Phase: Brief Ideas; 5.5 Adaptive Classifier; 5.5.1 Failures Under Challenging Lighting Conditions; 5.5.2 Hybrid Intensity Averaging; 5.5.3 Parameter Adaptation; 5.6 Tracking and Search Minimization; 5.6.1 Tracking Considerations; 5.6.2 Filter Modelling and Implementation; 5.7 Phase-Preserving Denoising; 5.8 Global Haar-Like Features; 5.8.1 Global Features vs. Local Features.
5.8.2 Dynamic Global Haar Features5.9 Boosting Cascades with Local and Global Features; 5.10 Experimental Results; 5.11 Concluding Remarks; 6 Driver Inattention Detection; 6.1 Introduction; 6.2 Asymmetric Appearance Models; 6.2.1 Model Implementation; 6.2.2 Asymmetric AAM; 6.3 Driver's Head-Pose and Gaze Estimation; 6.3.1 Optimized 2D to 3D Pose Modelling; 6.3.2 Face Registration by Fermat-Transform; 6.4 Experimental Results; 6.4.1 Pose Estimation; 6.4.2 Yawning Detection and Head Nodding; 6.5 Concluding Remarks; 7 Vehicle Detection and Distance Estimation; 7.1 Introduction.
7.2 Overview of Methodology7.3 Adaptive Global Haar Classifier; 7.4 Line and Corner Features; 7.4.1 Horizontal Edges; 7.4.2 Feature-Point Detection; 7.5 Detection Based on Taillights; 7.5.1 Taillight Specifications: Discussion; 7.5.2 Colour Spectrum Analysis; 7.5.3 Taillight Segmentation; 7.5.4 Taillight Pairing by Template Matching; 7.5.5 Taillight Pairing by Virtual Symmetry Detection; 7.6 Data Fusion and Temporal Information; 7.7 Inter-vehicle Distance Estimation; 7.8 Experimental Results; 7.8.1 Evaluations of Distance Estimation; 7.8.2 Evaluations of the Proposed Vehicle Detection.
Bibliography Note Includes bibliographical references and index.
Summary 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. 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.
Note Online resource; title from PDF title page (SpringerLink, viewed February 16, 2017).
ISBN 9783319505510 (electronic bk.)
3319505513 (electronic bk.)
9783319505497 (print)
3319505491
ISBN/ISSN 10.1007/978-3-319-50551-0
OCLC # 971613450
Additional Format Printed edition: 9783319505497