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: Tania Banerjee
Publisher: CRC Press
ISBN: 1000969770
Category : Computers
Languages : en
Pages : 213
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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: Padmesh Tripathi
Publisher: John Wiley & Sons
ISBN: 1394200587
Category : Computers
Languages : en
Pages : 373
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Book Description
This book gives comprehensive insights into the application of AI, machine learning, and deep learning in developing efficient and optimal surveillance systems for both indoor and outdoor environments, addressing the evolving security challenges in public and private spaces. Mathematical Models Using Artificial Intelligence for Surveillance Systems aims to collect and publish basic principles, algorithms, protocols, developing trends, and security challenges and their solutions for various indoor and outdoor surveillance applications using artificial intelligence (AI). The book addresses how AI technologies such as machine learning (ML), deep learning (DL), sensors, and other wireless devices could play a vital role in assisting various security agencies. Security and safety are the major concerns for public and private places in every country. Some places need indoor surveillance, some need outdoor surveillance, and, in some places, both are needed. The goal of this book is to provide an efficient and optimal surveillance system using AI, ML, and DL-based image processing. The blend of machine vision technology and AI provides a more efficient surveillance system compared to traditional systems. Leading scholars and industry practitioners are expected to make significant contributions to the chapters. Their deep conversations and knowledge, which are based on references and research, will result in a wonderful book and a valuable source of information.
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: Liang Wang
Publisher: Springer
ISBN: 9780857290588
Category : Computers
Languages : en
Pages : 372
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Book Description
Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
Author: Paramasivan, P.
Publisher: IGI Global
ISBN:
Category : Computers
Languages : en
Pages : 412
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Book Description
The rise of Artificial Intelligence (AI) amidst the backdrop of a world that is changing at lightning speed presents a whole new set of challenges. One of our biggest hurdles is more transparency in AI solutions. It's a complex issue, but one that we need to address if we want to ensure that the benefits of AI are accessible to everyone. Across diverse sectors such as healthcare, surveillance, and human-computer interaction, the inability to understand and evaluate AI's decision-making processes hinders progress and raises concerns about accountability. Cross-Industry AI Applications is a groundbreaking solution to illuminate the mysteries of AI-driven human behavior analysis. This pioneering book addresses the necessity of transparency and explainability in AI systems, particularly in analyzing human behavior. Compiling insights from leading experts and academics offers a comprehensive exploration of state-of-the-art methodologies, benchmarks, and systems for understanding and predicting human behavior using AI. Each chapter delves into novel approaches and real-world applications, from facial expressions to gesture recognition, providing invaluable knowledge for scholars and professionals alike.
Author: Ashish Kumar
Publisher: Springer Nature
ISBN: 9819932882
Category : Computers
Languages : en
Pages : 280
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Book Description
With the increase in urban population, it became necessary to keep track of the object of interest. In favor of SDGs for sustainable smart city, with the advancement in technology visual tracking extends to track multi-target present in the scene rather estimating location for single target only. In contrast to single object tracking, multi-target introduces one extra step of detection. Tracking multi-target includes detecting and categorizing the target into multiple classes in the first frame and provides each individual target an ID to keep its track in the subsequent frames of a video stream. One category of multi-target algorithms exploits global information to track the target of the detected target. On the other hand, some algorithms consider present and past information of the target to provide efficient tracking solutions. Apart from these, deep leaning-based algorithms provide reliable and accurate solutions. But, these algorithms are computationally slow when applied in real-time. This book presents and summarizes the various visual tracking algorithms and challenges in the domain. The various feature that can be extracted from the target and target saliency prediction is also covered. It explores a comprehensive analysis of the evolution from traditional methods to deep learning methods, from single object tracking to multi-target tracking. In addition, the application of visual tracking and the future of visual tracking can also be introduced to provide the future aspects in the domain to the reader. This book also discusses the advancement in the area with critical performance analysis of each proposed algorithm. This book will be formulated with intent to uncover the challenges and possibilities of efficient and effective tracking of single or multi-object, addressing the various environmental and hardware challenges. The intended audience includes academicians, engineers, postgraduate students, developers, professionals, military personals, scientists, data analysts, practitioners, and people who are interested in exploring more about tracking.· Another projected audience are the researchers and academicians who identify and develop methodologies, frameworks, tools, and applications through reference citations, literature reviews, quantitative/qualitative results, and discussions.
Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Artificial intelligence
Languages : en
Pages : 320
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Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author: Singh, Chandra
Publisher: IGI Global
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 551
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Book Description
The fusion of drones and Industry 5.0 has emerged as a transformative force, redefining the landscape of industrial progress. Drone Applications for Industry 5.0 reveals the strong connection between drones and Industry 5.0, exploring how they come together to blend human skills with automated precision. As we stand on the horizon of the fifth industrial revolution, Industry 5.0 uniquely celebrates the return of the human touch, harmonizing the strengths of machines with human intuition and empathy. Drones play a pivotal role in shaping this evolutionary transition. The narrative unfolds against the backdrop of historical industrial revolutions, each marked by radical transformations. Unlike its predecessors, Industry 5.0 places humans at the center, emphasizing collaboration with machines. Drones have matured into invaluable instruments with applications spanning manufacturing, agriculture, transportation, and emergency services. Drone Applications for Industry 5.0 embarks on a journey, guiding scholars, researchers, and students through the foundations of Industry 5.0 and the mechanics of drones. It explores practical uses in various fields, offering both theory and practical insights which empowers professionals to fully utilize drones.
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
