Vision-based Pedestrian Protection Systems for Intelligent Vehicles
Vision-based Pedestrian Protection Systems for Intelligent Vehicles

Author: David Gerónimo

Publisher: Springer Science & Business Media

Published: 2013-08-31

Total Pages: 118

ISBN-13: 1461479878

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Pedestrian Protection Systems (PPSs) are on-board systems aimed at detecting and tracking people in the surroundings of a vehicle in order to avoid potentially dangerous situations. These systems, together with other Advanced Driver Assistance Systems (ADAS) such as lane departure warning or adaptive cruise control, are one of the most promising ways to improve traffic safety. By the use of computer vision, cameras working either in the visible or infra-red spectra have been demonstrated as a reliable sensor to perform this task. Nevertheless, the variability of human’s appearance, not only in terms of clothing and sizes but also as a result of their dynamic shape, makes pedestrians one of the most complex classes even for computer vision. Moreover, the unstructured changing and unpredictable environment in which such on-board systems must work makes detection a difficult task to be carried out with the demanded robustness. In this brief, the state of the art in PPSs is introduced through the review of the most relevant papers of the last decade. A common computational architecture is presented as a framework to organize each method according to its main contribution. More than 300 papers are referenced, most of them addressing pedestrian detection and others corresponding to the descriptors (features), pedestrian models, and learning machines used. In addition, an overview of topics such as real-time aspects, systems benchmarking and future challenges of this research area are presented.

Vision-based Pedestrian Protection Systems for Intelligent Vehicles
Vision-based Pedestrian Protection Systems for Intelligent Vehicles

Author: David Geronimo

Publisher: Springer

Published: 2013-09-01

Total Pages: 114

ISBN-13: 9781461479888

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Pedestrian Protection Systems (PPSs) are on-board systems aimed at detecting and tracking people in the surroundings of a vehicle in order to avoid potentially dangerous situations. These systems, together with other Advanced Driver Assistance Systems (ADAS) such as lane departure warning or adaptive cruise control, are one of the most promising ways to improve traffic safety. By the use of computer vision, cameras working either in the visible or infra-red spectra have been demonstrated as a reliable sensor to perform this task. Nevertheless, the variability of human’s appearance, not only in terms of clothing and sizes but also as a result of their dynamic shape, makes pedestrians one of the most complex classes even for computer vision. Moreover, the unstructured changing and unpredictable environment in which such on-board systems must work makes detection a difficult task to be carried out with the demanded robustness. In this brief, the state of the art in PPSs is introduced through the review of the most relevant papers of the last decade. A common computational architecture is presented as a framework to organize each method according to its main contribution. More than 300 papers are referenced, most of them addressing pedestrian detection and others corresponding to the descriptors (features), pedestrian models, and learning machines used. In addition, an overview of topics such as real-time aspects, systems benchmarking and future challenges of this research area are presented.

Green, Smart and Connected Transportation Systems
Green, Smart and Connected Transportation Systems

Author: Wuhong Wang

Publisher: Springer Nature

Published: 2020-03-23

Total Pages: 1505

ISBN-13: 9811506442

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These proceedings gather selected papers from the 9th International Conference on Green Intelligent Transportation Systems and Safety, held in Guilin, China on July 1-3, 2018. They feature cutting-edge studies on Green Intelligent Mobility Systems, the guiding motto being to achieve “green, intelligent, and safe transportation systems.” The contributions presented here can help promote the development of green mobility and intelligent transportation technologies to improve interconnectivity, resource sharing, flexibility and efficiency. Given its scope, the book will benefit researchers and engineers in the fields of Transportation Technology and Traffic Engineering, Automotive and Mechanical Engineering, Industrial and System Engineering, and Electrical Engineering alike.

Computer Vision in Vehicle Technology
Computer Vision in Vehicle Technology

Author: Antonio M. López

Publisher: John Wiley & Sons

Published: 2017-02-08

Total Pages: 252

ISBN-13: 1118868056

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A unified view of the use of computer vision technology for different types of vehicles Computer Vision in Vehicle Technology focuses on computer vision as on-board technology, bringing together fields of research where computer vision is progressively penetrating: the automotive sector, unmanned aerial and underwater vehicles. It also serves as a reference for researchers of current developments and challenges in areas of the application of computer vision, involving vehicles such as advanced driver assistance (pedestrian detection, lane departure warning, traffic sign recognition), autonomous driving and robot navigation (with visual simultaneous localization and mapping) or unmanned aerial vehicles (obstacle avoidance, landscape classification and mapping, fire risk assessment). The overall role of computer vision for the navigation of different vehicles, as well as technology to address on-board applications, is analysed. Key features: Presents the latest advances in the field of computer vision and vehicle technologies in a highly informative and understandable way, including the basic mathematics for each problem. Provides a comprehensive summary of the state of the art computer vision techniques in vehicles from the navigation and the addressable applications points of view. Offers a detailed description of the open challenges and business opportunities for the immediate future in the field of vision based vehicle technologies. This is essential reading for computer vision researchers, as well as engineers working in vehicle technologies, and students of computer vision.

Intelligent Vehicles
Intelligent Vehicles

Author: Felipe Jiménez

Publisher: Butterworth-Heinemann

Published: 2017-09-08

Total Pages: 505

ISBN-13: 012813108X

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Intelligent Road Vehicles examines specific aspects of intelligent vehicles such as enabling technologies, human factors and an analysis of social and economic impacts. The book is an invaluable resource for those pursuing deeper knowledge in the intelligent vehicles field, providing readers with an idea of current and future technologies, current projects and developments and the future of intelligent vehicles. Intelligent road vehicles are becoming a challenging area of research worldwide. Apart from the final applications and systems in vehicles, there are many enabling technologies that should be introduced. Communications and automation are two key areas for future automobiles. This book benefits from collaboration on the Thematic Network on Intelligent Vehicles led by Felipe Jimenez. - Provides a general overview of different aspects related to intelligent road vehicles (sensors, applications, communications, automation, human factors, etc.) - Addresses the different components and building blocks of intelligent vehicles in a single, comprehensive reference - Explains how sensors are interpreted, including how different sensor readings are fused - Addresses issues involved with avoiding collisions and other factors such as pot holes, unclear road lines or markings, and unexpected weather conditions

Computer Vision for Driver Assistance
Computer Vision for Driver Assistance

Author: Mahdi Rezaei

Publisher: Springer

Published: 2017-02-06

Total Pages: 236

ISBN-13: 3319505513

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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. 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.

Computational Intelligence in Automotive Applications
Computational Intelligence in Automotive Applications

Author: Danil Prokhorov

Publisher: Springer

Published: 2008-05-28

Total Pages: 288

ISBN-13: 3540792570

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What is computational intelligence (CI)? Traditionally, CI is understood as a collection of methods from the ?elds of neural networks (NN), fuzzy logic and evolutionary computation. Various de?nitions and opinions exist, but what belongs to CI is still being debated; see, e.g., [1–3]. More recently there has been a proposal to de?ne the CI not in terms of the tools but in terms of challenging problems to be solved [4]. With this edited volume I have made an attempt to give a representative sample of contemporary CI activities in automotive applications to illustrate the state of the art. While CI researchand achievements in some specialized ?elds described (see, e.g., [5, 6]), this is the ?rst volume of its kind dedicated to automotive technology. As if re?ecting the general lack of consensus on what constitutes the ?eld of CI, this volume 1 illustrates automotive applications of not only neural and fuzzy computations which are considered to be the “standard” CI topics, but also others, such as decision trees, graphicalmodels, Support Vector Machines (SVM), multi-agent systems, etc. This book is neither an introductory text, nor a comprehensive overview of all CI research in this area. Hopefully, as a broad and representative sample of CI activities in automotive applications, it will be worth reading for both professionals and students. When the details appear insu?cient, the reader is encouraged to consult other relevant sources provided by the chapter authors.

Communication, Cloud and Big Data
Communication, Cloud and Big Data

Author: Hiren Kumar Deva Sarma

Publisher: ACCB Publishing

Published: 2014-12-31

Total Pages: 167

ISBN-13: 1908368039

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Analysis of big data is becoming a hot stuff for engineers, researchers and business enterprises now a days. It refers to the process of collecting, organizing and analyzing large sets of data to discover hidden patterns and other useful information. Not solely can massive information analytics assist to know the knowledge contained inside the information, however it will additionally facilitate to determine the information that is most significant to the business and future business choices. Cloud computing is the type of computing that relies on sharing computing resources rather than having local servers or personal devices to handle applications. Cloud computing aims at applying traditional supercomputing, or high-performance computing power to perform tens of trillions of computations per second, in consumer-oriented applications such as financial portfolios, to deliver personalized information, to provide data storage etc. Since big data places on networks, storage and servers, requirements arise to analyse this huge amount data on the cloud. Even cloud providers also welcome this new business opportunity of supporting big data analysis in the cloud. But in the same time they are facing various, architectural and technical hurdles. Therefore, big data analysis in cloud attacting many researchers now a days. The National Conference on Communication, Cloud and Big Data (CCB) 2014 organized by Department of Information Technology, SMIT has received keen response from researchers across the country. Each paper went through reviews process and finally, 30 papers were selected for presentation. The papers are an even mix of research topics from the fields of Communication, Cloud and Big Data and its applications in various fields of engineering and science.

Pattern Recognition and Image Analysis
Pattern Recognition and Image Analysis

Author: Roberto Paredes

Publisher: Springer

Published: 2015-06-09

Total Pages: 756

ISBN-13: 3319193902

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This book constitutes the proceedings of the 7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015, held in Santiage de Compostela, Spain, in June 2015. The 83 papers presented in this volume were carefully reviewed and selected from 141 submissions. They were organized in topical sections named: Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; Applications; Medical Image; Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; and Applications

Context-Enhanced Information Fusion
Context-Enhanced Information Fusion

Author: Lauro Snidaro

Publisher: Springer

Published: 2016-05-25

Total Pages: 696

ISBN-13: 3319289713

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This text reviews the fundamental theory and latest methods for including contextual information in fusion process design and implementation. Chapters are contributed by the foremost international experts, spanning numerous developments and applications. The book highlights high- and low-level information fusion problems, performance evaluation under highly demanding conditions, and design principles. A particular focus is placed on approaches that integrate research from different communities, emphasizing the benefit of combining different techniques to overcome the limitations of a single perspective. Features: introduces the terminology and core elements in information fusion and context; presents key themes for context-enhanced information fusion; discusses design issues in developing context-aware fusion systems; provides mathematical grounds for modeling the contextual influences in representative fusion problems; describes the fusion of hard and soft data; reviews a diverse range of applications.