TY  - BOOK
AU  - Rezaei,Mahdi
AU  - Klette,Reinhard
TI  - Computer vision for driver assistance: simultaneous traffic and driver monitoring 
T2  - Computational imaging and vision,
SN  - 3319505491
AV  - TL272.57 
U1  - 629.27 23
PY  - 2017///]
CY  - Cham, Switzerland
PB  - Springer
KW  - Driver assistance systems
KW  - Computer vision
N1  - Includes bibliographical references and index; Preface --; 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 Optimization --; 3.7; Stereo Matching --; 4; Object Detection, Classification, and Tracking --; 4.1; Object Detection and Classification --; 4.2; Supervised Classification Techniques --; 4.3; Unsupervised Classification Techniques --; 4.4; Object Tracking --; 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 --; 5.6; Tracking and Search Minimization --; 5.7; Phase-Preserving Denoising --; 5.8; Global Haar-Like Features --; 5.9; 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 --; 6.3; Driver's Head-Pose and Gaze Estimation --; 6.4; Experimental Results --; 6.5; Concluding Remarks --; 7; Vehicle Detection and Distance Estimation --; 7.1; Introduction --; 7.2; Overview of Methodology --; 7.3; Adaptive Global Haar Classifier --; 7.4; Line and Corner Features --; 7.5; Detection Based on Taillights --; 7.6; Data Fusion and Temporal Information --; 7.7; Inter-vehicle Distance Estimation --; 7.8; Experimental Results --; 8; Fuzzy Fusion for Collision Avoidance --; 8.1; Introduction --; 8.2; System Components --; 8.3; Fuzzifier and Membership Functions --; 8.4; Fuzzy Interference and Fusion Engine --; 8.5; Defuzzification --; 8.6; Experimental Results --; 8.7; Concluding Remarks --; Bibliography --; Index
N2  - "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."--Publisher's website
ER  -