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Computer Vision for Driver Assistance: Simultaneous Traffic and Driver Monitoring 1st ed. 2017 [Kõva köide]

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  • Formaat: Hardback, 224 pages, kõrgus x laius: 235x155 mm, kaal: 4843 g, 137 Illustrations, color; 2 Illustrations, black and white; XVI, 224 p. 139 illus., 137 illus. in color., 1 Hardback
  • Sari: Computational Imaging and Vision 45
  • Ilmumisaeg: 15-Feb-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319505491
  • ISBN-13: 9783319505497
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Computer Vision for Driver Assistance: Simultaneous Traffic and Driver Monitoring 1st ed. 2017Suurem pilt
  • Formaat: Hardback, 224 pages, kõrgus x laius: 235x155 mm, kaal: 4843 g, 137 Illustrations, color; 2 Illustrations, black and white; XVI, 224 p. 139 illus., 137 illus. in color., 1 Hardback
  • Sari: Computational Imaging and Vision 45
  • Ilmumisaeg: 15-Feb-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319505491
  • ISBN-13: 9783319505497
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319505497.html
Märksõnad:
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 des

ign.

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.

Arvustused

This book is a first as it integrates the concepts and techniques of computer vision with those of ADAS. Practitioners, professors, and graduate students will appreciate the breath of the topics covered, the depth of analysis provided by the experiments, and the accessible writing style adopted by the authors. (Computing Reviews, December, 2017)

1 Vision-Based Driver-Assistance Systems
1(18)
1.1 Driver-Assistance Towards Autonomous Driving
1(1)
1.2 Sensors
2(2)
1.3 Vision-Based Driver Assistance
4(3)
1.4 Safety and Comfort Functionalities
7(1)
1.5 VB-DAS Examples
8(4)
1.6 Current Developments
12(3)
1.7 Scope of the Book
15(4)
2 Driver-Environment Understanding
19(18)
2.1 Driver and Environment
19(1)
2.2 Driver Monitoring
20(5)
2.3 Basic Environment Monitoring
25(5)
2.4 Midlevel Environment Perception
30(7)
3 Computer Vision Basics
37(14)
3.1 Image Notations
37(2)
3.2 The Integral Image
39(1)
3.3 RGB to HSV Conversion
40(1)
3.4 Line Detection by Hough Transform
41(2)
3.5 Cameras
43(1)
3.6 Stereo Vision and Energy Optimization
44(3)
3.7 Stereo Matching
47(4)
4 Object Detection, Classification, and Tracking
51(44)
4.1 Object Detection and Classification
51(2)
4.2 Supervised Classification Techniques
53(15)
4.2.1 The Support Vector Machine
53(6)
4.2.2 The Histogram of Oriented Gradients
59(4)
4.2.3 Haar-Like Features
63(5)
4.3 Unsupervised Classification Techniques
68(10)
4.3.1 k-Means Clustering
68(4)
4.3.2 Gaussian Mixture Models
72(6)
4.4 Object Tracking
78(17)
4.4.1 Mean Shift
80(4)
4.4.2 Continuously Adaptive Mean Shift
84(1)
4.4.3 The Kanade--Lucas--Tomasi (KLT) Tracker
85(4)
4.4.4 Kalman Filter
89(6)
5 Driver Drowsiness Detection
95(32)
5.1 Introduction
95(2)
5.2 Training Phase: The Dataset
97(2)
5.3 Boosting Parameters
99(1)
5.4 Application Phase: Brief Ideas
99(3)
5.5 Adaptive Classifier
102(5)
5.5.1 Failures Under Challenging Lighting Conditions
102(2)
5.5.2 Hybrid Intensity Averaging
104(1)
5.5.3 Parameter Adaptation
105(2)
5.6 Tracking and Search Minimization
107(3)
5.6.1 Tracking Considerations
107(1)
5.6.2 Filter Modelling and Implementation
108(2)
5.7 Phase-Preserving Denoising
110(1)
5.8 Global Haar-Like Features
111(3)
5.8.1 Global Features vs. Local Features
112(2)
5.8.2 Dynamic Global Haar Features
114(1)
5.9 Boosting Cascades with Local and Global Features
114(1)
5.10 Experimental Results
115(10)
5.11 Concluding Remarks
125(2)
6 Driver Inattention Detection
127(20)
6.1 Introduction
127(2)
6.2 Asymmetric Appearance Models
129(4)
6.2.1 Model Implementation
129(2)
6.2.2 Asymmetric AAM
131(2)
6.3 Driver's Head-Pose and Gaze Estimation
133(6)
6.3.1 Optimized 2D to 3D Pose Modelling
134(2)
6.3.2 Face Registration by Fermat-Transform
136(3)
6.4 Experimental Results
139(5)
6.4.1 Pose Estimation
139(1)
6.4.2 Yawning Detection and Head Nodding
139(5)
6.5 Concluding Remarks
144(3)
7 Vehicle Detection and Distance Estimation
147(42)
7.1 Introduction
147(2)
7.2 Overview of Methodology
149(3)
7.3 Adaptive Global Haar Classifier
152(3)
7.4 Line and Corner Features
155(4)
7.4.1 Horizontal Edges
156(1)
7.4.2 Feature-Point Detection
157(2)
7.5 Detection Based on Taillights
159(9)
7.5.1 Taillight Specifications: Discussion
159(2)
7.5.2 Colour Spectrum Analysis
161(1)
7.5.3 Taillight Segmentation
162(1)
7.5.4 Taillight Pairing by Template Matching
163(2)
7.5.5 Taillight Pairing by Virtual Symmetry Detection
165(3)
7.6 Data Fusion and Temporal Information
168(3)
7.7 Inter-vehicle Distance Estimation
171(3)
7.8 Experimental Results
174(13)
7.8.1 Evaluations of Distance Estimation
175(1)
7.8.2 Evaluations of the Proposed Vehicle Detection
176(11)
7.9 Concluding Remarks
187(2)
8 Fuzzy Fusion for Collision Avoidance
189(18)
8.1 Introduction
189(2)
8.2 System Components
191(1)
8.3 Fuzzifier and Membership Functions
192(3)
8.4 Fuzzy Inference and Fusion Engine
195(2)
8.4.1 Rule of Implication
196(1)
8.4.2 Rule of Aggregation
196(1)
8.5 Denazification
197(1)
8.6 Experimental Results
197(7)
8.7 Concluding Remarks
204(3)
Bibliography 207(14)
Index 221
Mahdi Rezaei is Assistant Professor at Qazvin Islamic Azad University, Iran, and Honorary Academic Staff at the University of Auckland, New Zealand. He has a PhD in Computer Science and was awarded the Best Thesis Award from the University of Auckland. His research interests include computer vision, pattern recognition, and advanced driver assistance systems. Rezaei is the author of numerous contributions to top publications, including IEEE Transactions on Intelligent Transportation Systems and IEEE Conference on Computer Vision and Pattern Recognition, CVPR.

Reinhard Klette, Fellow of the Royal Society of New Zealand, is Professor at the Auckland University of Technology, New Zealand. He previously held positions at the University of Auckland, the Technical University of Berlin, and the Academy of Sciences Berlin. His research interests include computer vision, pattern recognition, and algorithm design. From 2003 to 2008, he was Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence.