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000779446 019__ $$a981850943
000779446 020__ $$a9783319505510$$q(electronic book)
000779446 020__ $$a3319505513$$q(electronic book)
000779446 020__ $$z9783319505497
000779446 0247_ $$a10.1007/978-3-319-50551-0$$2doi
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000779446 08204 $$a629.2/7$$223
000779446 08204 $$a510
000779446 1001_ $$aRezaei, Mahdi,$$eauthor.
000779446 24510 $$aComputer vision for driver assistance :$$bsimultaneous traffic and driver monitoring /$$cMahdi Rezaei, Reinhard Klette.
000779446 264_1 $$aCham, Switzerland :$$bSpringer,$$c2017.
000779446 300__ $$a1 online resource.
000779446 336__ $$atext$$btxt$$2rdacontent
000779446 337__ $$acomputer$$bc$$2rdamedia
000779446 338__ $$aonline resource$$bcr$$2rdacarrier
000779446 347__ $$atext file$$bPDF$$2rda
000779446 4901_ $$aComputational imaging and vision,$$x1381-6446 ;$$vvolume 45
000779446 504__ $$aIncludes bibliographical references and index.
000779446 5050_ $$aPreface; Contents; Symbols; 1 Vision-Based Driver-Assistance Systems; 1.1 Driver-Assistance Towards Autonomous Driving; 1.2 Sensors; 1.3 Vision-Based Driver Assistance; 1.4 Safety and Comfort Functionalities; 1.5 VB-DAS Examples; 1.6 Current Developments; 1.7 Scope of the Book; 2 Driver-Environment Understanding; 2.1 Driver and Environment; 2.2 Driver Monitoring; 2.3 Basic Environment Monitoring; 2.4 Midlevel Environment Perception; 3 Computer Vision Basics; 3.1 Image Notations; 3.2 The Integral Image; 3.3 RGB to HSV Conversion; 3.4 Line Detection by Hough Transform; 3.5 Cameras
000779446 5058_ $$a3.6 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
000779446 5058_ $$a4.4.4.1 Filter Implementation4.4.4.2 Tracking by Prediction and Refinement; 5 Driver Drowsiness Detection; 5.1 Introduction; 5.2 Training Phase: The Dataset; 5.3 Boosting Parameters; 5.4 Application Phase: Brief Ideas; 5.5 Adaptive Classifier; 5.5.1 Failures Under Challenging Lighting Conditions; 5.5.2 Hybrid Intensity Averaging; 5.5.3 Parameter Adaptation; 5.6 Tracking and Search Minimization; 5.6.1 Tracking Considerations; 5.6.2 Filter Modelling and Implementation; 5.7 Phase-Preserving Denoising; 5.8 Global Haar-Like Features; 5.8.1 Global Features vs. Local Features
000779446 5058_ $$a5.8.2 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
000779446 5058_ $$a7.2 Overview of Methodology7.3 Adaptive Global Haar Classifier; 7.4 Line and Corner Features; 7.4.1 Horizontal Edges; 7.4.2 Feature-Point Detection; 7.5 Detection Based on Taillights; 7.5.1 Taillight Specifications: Discussion; 7.5.2 Colour Spectrum Analysis; 7.5.3 Taillight Segmentation; 7.5.4 Taillight Pairing by Template Matching; 7.5.5 Taillight Pairing by Virtual Symmetry Detection; 7.6 Data Fusion and Temporal Information; 7.7 Inter-vehicle Distance Estimation; 7.8 Experimental Results; 7.8.1 Evaluations of Distance Estimation; 7.8.2 Evaluations of the Proposed Vehicle Detection
000779446 506__ $$aAccess limited to authorized users.
000779446 520__ $$aThis book summarises the state of the art in computer vision-based driver and road monitoring, focussing on monocular vision technology in particular, with the aim to address challenges of driver assistance and autonomous driving systems. While the systems designed for the assistance of drivers of on-road vehicles are currently converging to the design of autonomous vehicles, the research presented here focuses on scenarios where a driver is still assumed to pay attention to the traffic while operating a partially automated vehicle. Proposing various computer vision algorithms, techniques and methodologies, the authors also provide a general review of computer vision technologies that are relevant for driver assistance and fully autonomous vehicles. Computer Vision for Driver Assistance is the first book of its kind and will appeal to undergraduate and graduate students, researchers, engineers and those generally interested in computer vision-related topics in modern vehicle design. .
000779446 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 16, 2017).
000779446 650_0 $$aDriver assistance systems.
000779446 650_0 $$aComputer vision.
000779446 7001_ $$aKlette, Reinhard,$$eauthor.
000779446 77608 $$iPrint version:$$z9783319505497
000779446 830_0 $$aComputational imaging and vision ;$$vv. 45.
000779446 852__ $$bebk
000779446 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-50551-0$$zOnline Access$$91397441.1
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