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Author: Ruimin Ke
Publisher:
ISBN:
Category :
Languages : en
Pages : 222
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Book Description
Traffic cameras have the properties of being cost-effective, information-rich, and widely deployed, which are filling up a big gap in today0́9s traffic sensor needs. With the recent progress in traffic operations, information technology, and computer vision, traffic video analytics is driving a broad range of smart city applications with great potential to benefit future transportation and infrastructure systems. Most such applications, e.g., smart traffic surveillance and autonomous driving, require not only high intelligence but also real-time processing capability. Real-time video analytics is well-believed to be one of the most challenging yet most powerful applications for smart cities. It is often bottlenecked by the large volume of video data, high computational cost, and limited data communication bandwidth. This dissertation explores general guidelines and new traffic video analytical methods and systems towards high intelligence and real-time operations for roadway transportation. The designs focus on both the algorithm level and the application system level. On the one hand, lightweight methods are devised based on machine learning techniques and transportation domain knowledge for high smartness, accuracy, and efficiency in specific traffic scenarios. On the other hand, system architectures are developed by leveraging the power of edge computing so that we can split the computational workload between the centralized servers and local Internet-of-Things (IoT) devices for the purpose of system performance optimization. The traffic analytics products and findings in this dissertation can be applied to three transportation-related scenarios with different properties regarding video data collection and processing: (1) traffic surveillance, (2) vehicle onboard sensing, and (3) unmanned aerial vehicle (UAV) sensing. Correspondingly, they apply to three key components of modern intelligent transportation systems (ITS), i.e., smart infrastructures, intelligent vehicle, and aerial surveillance for road traffic. These components possess unique characteristics that can be utilized for video analytics, yet with different challenges to address. To this end, the dissertation proposes algorithms, frameworks, and field implementation examples of how to design and evaluate traffic video analytics systems for smart transportation applications towards high intelligence and efficiency. Experiments were conducted with real-world datasets and tests in a variety of scenarios. This dissertation is among the first efforts in developing edge computing applications for transportation and in exploring UAV sensing for traffic flow.
Author: Ruimin Ke
Publisher:
ISBN:
Category :
Languages : en
Pages : 222
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Book Description
Traffic cameras have the properties of being cost-effective, information-rich, and widely deployed, which are filling up a big gap in today0́9s traffic sensor needs. With the recent progress in traffic operations, information technology, and computer vision, traffic video analytics is driving a broad range of smart city applications with great potential to benefit future transportation and infrastructure systems. Most such applications, e.g., smart traffic surveillance and autonomous driving, require not only high intelligence but also real-time processing capability. Real-time video analytics is well-believed to be one of the most challenging yet most powerful applications for smart cities. It is often bottlenecked by the large volume of video data, high computational cost, and limited data communication bandwidth. This dissertation explores general guidelines and new traffic video analytical methods and systems towards high intelligence and real-time operations for roadway transportation. The designs focus on both the algorithm level and the application system level. On the one hand, lightweight methods are devised based on machine learning techniques and transportation domain knowledge for high smartness, accuracy, and efficiency in specific traffic scenarios. On the other hand, system architectures are developed by leveraging the power of edge computing so that we can split the computational workload between the centralized servers and local Internet-of-Things (IoT) devices for the purpose of system performance optimization. The traffic analytics products and findings in this dissertation can be applied to three transportation-related scenarios with different properties regarding video data collection and processing: (1) traffic surveillance, (2) vehicle onboard sensing, and (3) unmanned aerial vehicle (UAV) sensing. Correspondingly, they apply to three key components of modern intelligent transportation systems (ITS), i.e., smart infrastructures, intelligent vehicle, and aerial surveillance for road traffic. These components possess unique characteristics that can be utilized for video analytics, yet with different challenges to address. To this end, the dissertation proposes algorithms, frameworks, and field implementation examples of how to design and evaluate traffic video analytics systems for smart transportation applications towards high intelligence and efficiency. Experiments were conducted with real-world datasets and tests in a variety of scenarios. This dissertation is among the first efforts in developing edge computing applications for transportation and in exploring UAV sensing for traffic flow.
Author: El-Sayed M. El-Alfy
Publisher: CRC Press
ISBN: 1000851915
Category : Computers
Languages : en
Pages : 404
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Book Description
Video has rich information including meta-data, visual, audio, spatial and temporal data which can be analysed to extract a variety of low and high-level features to build predictive computational models using machine-learning algorithms to discover interesting patterns, concepts, relations, and associations. This book includes a review of essential topics and discussion of emerging methods and potential applications of video data mining and analytics. It integrates areas like intelligent systems, data mining and knowledge discovery, big data analytics, machine learning, neural network, and deep learning with focus on multimodality video analytics and recent advances in research/applications. Features: Provides up-to-date coverage of the state-of-the-art techniques in intelligent video analytics. Explores important applications that require techniques from both artificial intelligence and computer vision. Describes multimodality video analytics for different applications. Examines issues related to multimodality data fusion and highlights research challenges. Integrates various techniques from video processing, data mining and machine learning which has many emerging indoors and outdoors applications of smart cameras in smart environments, smart homes, and smart cities. This book aims at researchers, professionals and graduate students in image processing, video analytics, computer science and engineering, signal processing, machine learning, and electrical engineering.
Author: Abhishek Singh Rathore
Publisher: Bentham Science Publishers
ISBN: 981512370X
Category : Computers
Languages : en
Pages : 150
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Book Description
Video data analytics is rapidly evolving and transforming the way we live in urban environments. Video Data Analytics for Smart City Applications: Methods and Trends, data science experts present a comprehensive review of the latest advances and trends in video analytics technologies and their extensive applications in smart city planning and engineering. The book covers a wide range of topics including object recognition, action recognition, violence detection, and tracking, exploring deep learning approaches and other techniques for video data analytics. It also discusses the key enabling technologies for smart cities and homes and the scope and application of smart agriculture in smart cities. Moreover, the book addresses the challenges and security issues in terahertz band for wireless communication and the empirical impact of AI and IoT on performance management. One contribution also provides a review of the progress in achieving the Jal Jeevan Mission Goals for institutional capacity building in the Indian State of Chhattisgarh. For researchers, computer scientists, data analytics professionals, smart city planners and engineers, this book provides detailed references for further reading and demonstrates how technologies are serving their use-cases in the smart city. The book highlights the advances and trends in video analytics technologies and extensively addresses key themes, making it an essential resource for anyone looking to gain a comprehensive understanding of video data analytics for smart city applications.
Author: Tania Banerjee
Publisher: CRC Press
ISBN: 1000969703
Category : Computers
Languages : en
Pages : 194
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Book Description
Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts
Author: Tania Banerjee
Publisher:
ISBN: 9781003431176
Category : COMPUTERS
Languages : en
Pages : 0
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Book Description
Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts
Author: Tania Banerjee (Computer scientist)
Publisher:
ISBN: 9781032565170
Category : Computers
Languages : en
Pages : 0
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Book Description
"Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development"--
Author: Yifan Zhuang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The development of economics and technologies has promoted urbanization worldwide. Urbanization has brought great convenience to daily life. The fast construction of transportation facilities provides various means of transportation for everyday commuting. However, the growing traffic volume has threatened the existing transportation system by raising more traffic safety and congestion issues. Therefore, it is urgent and necessary to implement ITS with dynamic sensing and adjustment abilities. ITS shows great potential to improve traffic safety and efficiency, empowered by advanced IoT and AI. Within this system, the urban sensing and data analysis modules play an essential role in providing primary traffic information for follow-up works, including traffic prediction, operation optimization, and urban planning. Cameras and computer vision algorithms are the most popular toolkit in traffic sensing and analysis tasks. Deep learning-based computer vision algorithms have succeeded in multiple traffic sensing and analysis tasks, e.g., vehicle counting and crowd motion detection. The large-scale deployment of the sensor network and applications of deep learning algorithms significantly magnify previous methods' flaws, which hinder the further expansion of ITS. Firstly, the large-scale sensors and various tasks bring massive data and high workloads for data analysis on central servers. In contrast, annotated data for deep learning training in different tasks is insufficient, which leads to poor generalization when transferring to another application scenario. Additionally, traffic sensing faces adverse conditions with insufficient data and analysis qualities. This dissertation works on proposing efficient and robust machine learning methods for challenging traffic video sensing applications by presenting a systematic and practical workflow to optimize algorithm accuracy and efficiency. This dissertation first considers the high data volume challenge by designing a compression and knowledge distillation pipeline to reduce the model complexity and maintain accuracy. After applying the proposed pipeline, it is possible to further use the optimized algorithm on edge devices. This pipeline also works as the optimization foundation in the remaining works of this dissertation. Besides high data volume for analysis, insufficient training data is a considerable problem when deploying deep learning in practice. This dissertation has focused on two representative scenarios related to public safety – detecting and tracking small-scale persons in crowds and detecting rare objects in autonomous driving. Data augmentation and FSL strategies have been applied to increase the robustness of the machine learning system with limited training data. Finally, traffic sensing targets 24/7 stable operation, even in adverse conditions that reduce visibility and increase image noise with the RGB camera. Sensor fusion by combining RGB and infrared cameras is studied to improve accuracy in all light conditions. In conclusion, urbanization has simultaneously brought opportunities and challenges to the transportation system. ITS shows great potential to take this development chance and handle these challenges. This dissertation works on three data-oriented challenges and improves the accuracy and efficiency of vision-based traffic sensing algorithms. Several ITS applications are explored to demonstrate the effectiveness of the proposed methods, which achieve state-of-the-art accuracy and are far more efficient. In the future, additional research works can be explored based on this dissertation. With the continuing expansion of the sensor network, edge computing will be a more suitable system framework than cloud computing. Binary quantization and hardware-specific operator optimization can contribute to edge computing. Since data insufficiency is common in other transportation applications besides traffic detection, FSL will elevate traffic pattern forecasting and event analysis with a sequence model. For crowd monitoring, the next step will be motion prediction in bird's-eye view based on motion detection results.
Author: Guanghui Zhao
Publisher: World Scientific
ISBN: 9811236011
Category : Technology & Engineering
Languages : en
Pages : 352
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Book Description
This book is designed as a popular science book on big data analytics in intelligent transportation systems. It aims to provide an introduction to big-data transportation starting from an overview on the development of big data transportation in various countries. This is followed by a discussion on the blueprint strategies of big data transportation which include innovative models, planning, transportation logistics, and application case studies. Finally, the book discusses applications of big data transportation platforms.
Author: Pethuru Raj Chelliah
Publisher: John Wiley & Sons
ISBN: 1119985609
Category : Computers
Languages : en
Pages : 516
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Book Description
The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry Comprehensive resource describing how operations, outputs, and offerings of the oil and gas industry can improve via advancements in AI The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry describes the proven and promising digital technologies and tools available to empower the oil and gas industry to be future-ready. It shows how the widely reported limitations of the oil and gas industry are being nullified through the application of breakthrough digital technologies and how the convergence of digital technologies helps create new possibilities and opportunities to take this industry to its next level. The text demonstrates how scores of proven digital technologies, especially in AI, are useful in elegantly fulfilling complicated requirements such as process optimization, automation and orchestration, real-time data analytics, productivity improvement, employee safety, predictive maintenance, yield prediction, and accurate asset management for the oil and gas industry. The text differentiates and delivers sophisticated use cases for the various stakeholders, providing easy-to-understand information to accurately utilize proven technologies towards achieving real and sustainable industry transformation. The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry includes information on: How various machine and deep learning (ML/DL) algorithms, the prime modules of AI, empower AI systems to deliver on their promises and potential Key use cases of computer vision (CV) and natural language processing (NLP) as they relate to the oil and gas industry Smart leverage of AI, the Industrial Internet of Things (IIoT), cyber physical systems, and 5G communication Event-driven architecture (EDA), microservices architecture (MSA), blockchain for data and device security, and digital twins Clearly expounding how the power of AI and other allied technologies can be meticulously leveraged by the oil and gas industry, The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry is an essential resource for students, scholars, IT professionals, and business leaders in many different intersecting fields.
Author: Rajest, S. Suman
Publisher: IGI Global
ISBN:
Category : Computers
Languages : en
Pages : 618
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Book Description
The numerous developments in wireless communications and artificial intelligence (AI) have recently transformed the Internet of Things (IoT) networks to a level of connectivity and intelligence beyond any prior design. This topology is sharply exemplified in mobile edge computing, smart cities, smart homes, smart grids, and the IoT, among many other intelligent applications. Intelligent networks are founded on integrating caching and multi-agent systems that optimize data storage and the entire devices learning process. However, a central node through which all agents transmit status messages and reward information is a major drawback of this design pattern. This central node condition instigates more communication overhead, potential data leakage, and the birth of data islands. To reverse this trend, using distributed optimization techniques and methodologies in cache-enabled multi-agent learning environments is increasingly beneficial. Advancing Intelligent Networks Through Distributed Optimization explains the current race for sophisticated and accurate distributed optimization in cache-enabled intelligent IoT networks given the need to make multi-agent learning converge faster and reduce communication overhead. These techniques will require innovative resource allocation strategies stretching from system training to caching, communication, and processing amongst millions of agents. This book combines the key recent research in these races into a single binder that can serve all the interested theoretical and practical scholars. The book focuses broadly on intelligent systems optimization trends. It identifies the various applications of advanced distributed optimization from manufacturing to medicine, agriculture and smart cities.
