Moving Vehicle Detection In Traffic Surveillance Using Mamoving Vehicle Detection In Traffic Surveillance Using Machine Learning Techniqueschine Learning Techniques

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Video Based Machine Learning for Traffic Intersections

Author : Tania Banerjee
Publisher : CRC Press
ISBN 13 : 1000969770
Total Pages : 213 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Video Based Machine Learning for Traffic Intersections by : Tania Banerjee

Download or read book Video Based Machine Learning for Traffic Intersections written by Tania Banerjee and published by CRC Press. This book was released on 2023-10-17 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Moving Objects Detection Using Machine Learning

Author : Navneet Ghedia
Publisher : Springer Nature
ISBN 13 : 3030909107
Total Pages : 91 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Moving Objects Detection Using Machine Learning by : Navneet Ghedia

Download or read book Moving Objects Detection Using Machine Learning written by Navneet Ghedia and published by Springer Nature. This book was released on 2022-01-01 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.

Traffic Mining Applied to Police Activities

Author : Fabio Leuzzi
Publisher : Springer
ISBN 13 : 3319756087
Total Pages : 161 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Traffic Mining Applied to Police Activities by : Fabio Leuzzi

Download or read book Traffic Mining Applied to Police Activities written by Fabio Leuzzi and published by Springer. This book was released on 2018-03-21 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents high-quality original contributions on the development of automatic traffic analysis systems that are able to not only anticipate traffic scenarios, but also understand the behavior of road users (vehicles, bikes, trucks, etc.) in order to provide better traffic management, prevent accidents and, potentially, identify criminal behaviors. Topics also include traffic surveillance and vehicle accident analysis using formal concept analysis, convolutional and recurrent neural networks, unsupervised learning and process mining. The content is based on papers presented at the 1st Italian Conference for the Traffic Police (TRAP), which was held in Rome in October 2017. This conference represents a targeted response to the challenges facing the police in connection with managing massive traffic data, finding patterns from historical datasets, and analyzing complex traffic phenomena in order to anticipate potential criminal behaviors. The book will appeal to researchers, practitioners and decision makers interested in traffic monitoring and analysis, traffic modeling and simulation, mobility and social data mining, as well as members of the police.

Deep Learning based Vehicle Detection in Aerial Imagery

Author : Sommer, Lars Wilko
Publisher : KIT Scientific Publishing
ISBN 13 : 3731511134
Total Pages : 276 pages
Book Rating : 4.7/5 (315 download)

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Book Synopsis Deep Learning based Vehicle Detection in Aerial Imagery by : Sommer, Lars Wilko

Download or read book Deep Learning based Vehicle Detection in Aerial Imagery written by Sommer, Lars Wilko and published by KIT Scientific Publishing. This book was released on 2022-02-09 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes a novel deep learning based detection method, focusing on vehicle detection in aerial imagery recorded in top view. The base detection framework is extended by two novel components to improve the detection accuracy by enhancing the contextual and semantical content of the employed feature representation. To reduce the inference time, a lightweight CNN architecture is proposed as base architecture and a novel module that restricts the search area is introduced.

Data Traffic Monitoring and Analysis

Author : Ernst Biersack
Publisher : Springer
ISBN 13 : 3642367844
Total Pages : 370 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Data Traffic Monitoring and Analysis by : Ernst Biersack

Download or read book Data Traffic Monitoring and Analysis written by Ernst Biersack and published by Springer. This book was released on 2013-03-02 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book was prepared as the Final Publication of COST Action IC0703 "Data Traffic Monitoring and Analysis: theory, techniques, tools and applications for the future networks". It contains 14 chapters which demonstrate the results, quality,and the impact of European research in the field of TMA in line with the scientific objective of the Action. The book is structured into three parts: network and topology measurement and modelling, traffic classification and anomaly detection, quality of experience.

Road Traffic Modeling and Management

Author : Fouzi Harrou
Publisher : Elsevier
ISBN 13 : 0128234334
Total Pages : 270 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Road Traffic Modeling and Management by : Fouzi Harrou

Download or read book Road Traffic Modeling and Management written by Fouzi Harrou and published by Elsevier. This book was released on 2021-10-05 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. - Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring - Uses methods based on video and time series data for traffic modeling and forecasting - Includes case studies, key processes guidance and comparisons of different methodologies

Sensing Vehicle Conditions for Detecting Driving Behaviors

Author : Jiadi Yu
Publisher : Springer
ISBN 13 : 3319897705
Total Pages : 81 pages
Book Rating : 4.3/5 (198 download)

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Book Synopsis Sensing Vehicle Conditions for Detecting Driving Behaviors by : Jiadi Yu

Download or read book Sensing Vehicle Conditions for Detecting Driving Behaviors written by Jiadi Yu and published by Springer. This book was released on 2018-04-18 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief begins by introducing the concept of smartphone sensing and summarizing the main tasks of applying smartphone sensing in vehicles. Chapter 2 describes the vehicle dynamics sensing model that exploits the raw data of motion sensors (i.e., accelerometer and gyroscope) to give the dynamic of vehicles, including stopping, turning, changing lanes, driving on uneven road, etc. Chapter 3 detects the abnormal driving behaviors based on sensing vehicle dynamics. Specifically, this brief proposes a machine learning-based fine-grained abnormal driving behavior detection and identification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using the built-in motion sensors in smartphones. As more vehicles taking part in the transportation system in recent years, driving or taking vehicles have become an inseparable part of our daily life. However, increasing vehicles on the roads bring more traffic issues including crashes and congestions, which make it necessary to sense vehicle dynamics and detect driving behaviors for drivers. For example, sensing lane information of vehicles in real time can be assisted with the navigators to avoid unnecessary detours, and acquiring instant vehicle speed is desirable to many important vehicular applications. Moreover, if the driving behaviors of drivers, like inattentive and drunk driver, can be detected and warned in time, a large part of traffic accidents can be prevented. However, for sensing vehicle dynamics and detecting driving behaviors, traditional approaches are grounded on the built-in infrastructure in vehicles such as infrared sensors and radars, or additional hardware like EEG devices and alcohol sensors, which involves high cost. The authors illustrate that smartphone sensing technology, which involves sensors embedded in smartphones (including the accelerometer, gyroscope, speaker, microphone, etc.), can be applied in sensing vehicle dynamics and driving behaviors. Chapter 4 exploits the feasibility to recognize abnormal driving events of drivers at early stage. Specifically, the authors develop an Early Recognition system, ER, which recognize inattentive driving events at an early stage and alert drivers timely leveraging built-in audio devices on smartphones. An overview of the state-of-the-art research is presented in chapter 5. Finally, the conclusions and future directions are provided in Chapter 6.

Deep Learning for Autonomous Vehicle Control

Author : Sampo Kuutti
Publisher : Springer Nature
ISBN 13 : 3031015029
Total Pages : 70 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Deep Learning for Autonomous Vehicle Control by : Sampo Kuutti

Download or read book Deep Learning for Autonomous Vehicle Control written by Sampo Kuutti and published by Springer Nature. This book was released on 2022-06-01 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.

Video Based Machine Learning for Traffic Intersections

Author : Tania Banerjee
Publisher :
ISBN 13 : 9781003431176
Total Pages : 0 pages
Book Rating : 4.4/5 (311 download)

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Book Synopsis Video Based Machine Learning for Traffic Intersections by : Tania Banerjee

Download or read book Video Based Machine Learning for Traffic Intersections written by Tania Banerjee and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Real-Time Road Profile Identification and Monitoring

Author : Yechen Qin
Publisher : Springer Nature
ISBN 13 : 3031014995
Total Pages : 138 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Real-Time Road Profile Identification and Monitoring by : Yechen Qin

Download or read book Real-Time Road Profile Identification and Monitoring written by Yechen Qin and published by Springer Nature. This book was released on 2022-05-31 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ever stringent vehicle safety legislation and consumer expectations inspire the improvement of vehicle dynamic performance, which result in a rising number of control strategies for vehicle dynamics that rely on driving conditions. Road profiles, as the primary excitation source of vehicle systems, play a critical role in vehicle dynamics and also in public transportation. Knowledge of precise road conditions can thus be of great assistance for vehicle companies and government departments to develop proper dynamic control algorithms, and to fix roads in a timely manner and at the minimum cost, respectively. As a result, developing easy-to-use and accurate road estimation methods are of great importance in terms of reducing the cost related to vehicles and road maintenance as well as improving passenger comfort and handling capacity. A few books have already been published on road profile modeling and the influence of road unevenness on vehicle response. However, there is still room to discuss road assessment methods based on vehicle response and how road conditions can be used to improve vehicle dynamics. In this book, we use several generalized vehicle models to demonstrate the concepts, methods, and applications of vehicle response-based road estimation algorithms. In addition, necessary tools, algorithms, and methods are illustrated, and the benefits of the road estimation algorithms are evaluated. Furthermore, several case studies of controllable suspension systems to improve vehicle vertical dynamics are presented.

Machine Learning for Vision-Based Motion Analysis

Author : Liang Wang
Publisher : Springer
ISBN 13 : 9780857290588
Total Pages : 372 pages
Book Rating : 4.2/5 (95 download)

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Book Synopsis Machine Learning for Vision-Based Motion Analysis by : Liang Wang

Download or read book Machine Learning for Vision-Based Motion Analysis written by Liang Wang and published by Springer. This book was released on 2011-04-08 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advances in Human Activity Detection and Recognition (HADR) Systems

Author : Santosh Kumar Tripathy
Publisher : Springer Nature
ISBN 13 : 3031516605
Total Pages : 145 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Advances in Human Activity Detection and Recognition (HADR) Systems by : Santosh Kumar Tripathy

Download or read book Advances in Human Activity Detection and Recognition (HADR) Systems written by Santosh Kumar Tripathy and published by Springer Nature. This book was released on with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Learning-based VANET Communication and Security Techniques

Author : Liang Xiao
Publisher : Springer
ISBN 13 : 3030017311
Total Pages : 140 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Learning-based VANET Communication and Security Techniques by : Liang Xiao

Download or read book Learning-based VANET Communication and Security Techniques written by Liang Xiao and published by Springer. This book was released on 2018-10-29 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely book provides broad coverage of vehicular ad-hoc network (VANET) issues, such as security, and network selection. Machine learning based methods are applied to solve these issues. This book also includes four rigorously refereed chapters from prominent international researchers working in this subject area. The material serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues. This book will also help readers understand how to use machine learning to address the security and communication challenges in VANETs. Vehicular ad-hoc networks (VANETs) support vehicle-to-vehicle communications and vehicle-to-infrastructure communications to improve the transmission security, help build unmanned-driving, and support booming applications of onboard units (OBUs). The high mobility of OBUs and the large-scale dynamic network with fixed roadside units (RSUs) make the VANET vulnerable to jamming. The anti-jamming communication of VANETs can be significantly improved by using unmanned aerial vehicles (UAVs) to relay the OBU message. UAVs help relay the OBU message to improve the signal-to-interference-plus-noise-ratio of the OBU signals, and thus reduce the bit-error-rate of the OBU message, especially if the serving RSUs are blocked by jammers and/or interference, which is also demonstrated in this book. This book serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues.

Object Detection with Deep Learning Models

Author : S Poonkuntran
Publisher : CRC Press
ISBN 13 : 1000686795
Total Pages : 345 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Object Detection with Deep Learning Models by : S Poonkuntran

Download or read book Object Detection with Deep Learning Models written by S Poonkuntran and published by CRC Press. This book was released on 2022-11-01 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection

Deep Learning Based Vehicle Detection in Aerial Imagery

Author : Lars Sommer
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (112 download)

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Book Synopsis Deep Learning Based Vehicle Detection in Aerial Imagery by : Lars Sommer

Download or read book Deep Learning Based Vehicle Detection in Aerial Imagery written by Lars Sommer and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems

Author : Vipin Kumar Kukkala
Publisher : Springer Nature
ISBN 13 : 3031280164
Total Pages : 782 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems by : Vipin Kumar Kukkala

Download or read book Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems written by Vipin Kumar Kukkala and published by Springer Nature. This book was released on 2023-10-03 with total page 782 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive coverage of various solutions that address issues related to real-time performance, security, and robustness in emerging automotive platforms. The authors discuss recent advances towards the goal of enabling reliable, secure, and robust, time-critical automotive cyber-physical systems, using advanced optimization and machine learning techniques. The focus is on presenting state-of-the-art solutions to various challenges including real-time data scheduling, secure communication within and outside the vehicle, tolerance to faults, optimizing the use of resource-constrained automotive ECUs, intrusion detection, and developing robust perception and control techniques for increasingly autonomous vehicles.

Autonomous Intelligent Vehicles

Author : Hong Cheng
Publisher : Springer
ISBN 13 : 9781447122791
Total Pages : 154 pages
Book Rating : 4.1/5 (227 download)

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Book Synopsis Autonomous Intelligent Vehicles by : Hong Cheng

Download or read book Autonomous Intelligent Vehicles written by Hong Cheng and published by Springer. This book was released on 2011-11-16 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important text/reference presents state-of-the-art research on intelligent vehicles, covering not only topics of object/obstacle detection and recognition, but also aspects of vehicle motion control. With an emphasis on both high-level concepts, and practical detail, the text links theory, algorithms, and issues of hardware and software implementation in intelligent vehicle research. Topics and features: presents a thorough introduction to the development and latest progress in intelligent vehicle research, and proposes a basic framework; provides detection and tracking algorithms for structured and unstructured roads, as well as on-road vehicle detection and tracking algorithms using boosted Gabor features; discusses an approach for multiple sensor-based multiple-object tracking, in addition to an integrated DGPS/IMU positioning approach; examines a vehicle navigation approach using global views; introduces algorithms for lateral and longitudinal vehicle motion control.