Video Based Machine Learning for Traffic Intersections
暫譯: 基於視頻的交通路口機器學習
Banerjee, Tania, Huang, Xiaohui, Wu, Aotian
- 出版商: CRC
- 出版日期: 2023-10-17
- 售價: $4,480
- 貴賓價: 9.5 折 $4,256
- 語言: 英文
- 頁數: 168
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1032542268
- ISBN-13: 9781032542263
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相關分類:
Machine Learning
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商品描述
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
商品描述(中文翻譯)
《基於視頻的交通路口機器學習》描述了基於計算機視覺和機器學習的智能交通系統（ITS）應用的開發及其部署過程中遇到的挑戰。本書提出了幾種新穎的方法，包括用於車輛檢測、跟蹤和近失檢測的雙流卷積網絡架構；一種無監督的方法，利用深度學習模型結合相機校準和樣條映射方法來檢測魚眼路口視頻中的近失；以及利用視頻分析和信號定時數據準確檢測和分類事件的算法，這些事件基於行人-車輛和車輛-車輛互動中的相位和衝突類型。
本書利用實時軌跡預測方法，結合對齊的 Google Maps 資訊，來估算多個路口的車輛行駛時間。作者設計的新穎可視化軟體旨在為交通從業者提供服務，用於分析路口的效率和安全性。該軟體提供兩種模式：流媒體模式和歷史模式，這兩種模式對於需要快速分析軌跡以更好理解路口交通行為的交通工程師非常有用。
總體而言，本書全面概述了計算機視覺和機器學習在解決交通相關問題中的應用。《基於視頻的交通路口機器學習》展示了這些技術如何用於提高安全性、效率和交通流量，以及在問題發生之前識別潛在的衝突和問題。本書所呈現的一系列新穎方法和技術，為智能交通系統的研究和發展提供了令人興奮的可能性。
主要特點：
- 描述了智能交通系統（ITS）的開發及相關挑戰
- 提供了旨在幫助交通從業者分析路口效率和安全性的新穎可視化軟體
- 具有主動識別潛在衝突情況的潛力，並為實時車輛-車輛和行人-車輛衝突開發早期警報系統
作者簡介
Tania Banerjee, PhD, is a research assistant scientist in Computer and Information Science and Engineering at the University of Florida. She earned her PhD in Computer Science from the University of Florida in 2012. She completed her MSc in Mathematics from the Indian Institute of Technology, Kharagpur. Her research interests are video analytics, intelligent transportation, data compression, and high performance computing.
Xiaohui Huang, PhD, earned her PhD in the Department of Computer & Information Science & Engineering, University of Florida, Gainesville, Florida, in December, 2020. Her research interests include machine learning, computer vision, and intelligent transportation systems.
Aotian Wu is currently a PhD student in the Department of Computer & Information Science & Engineering, University of Florida, Gainesville, Florida. Her research interests are machine learning, computer vision, and intelligent transportation systems.
Ke Chen is currently a PhD student in the Department of Computer & Information Science & Engineering, University of Florida, Gainesville, Florida. His research interests are machine learning, computer architecture, operating systems and algorithms, and data structures.
Anand Rangarajan, PhD, is a Professor, Department of CISE, University of Florida. His research interests are machine learning, computer vision, medical and hyperspectral imaging, and the science of consciousness.
Sanjay Ranka, PhD, is a Distinguished Professor in the Department of Computer Information Science and Engineering at University of Florida. His current research interests are high performance computing and big data science with a focus on applications in CFD, healthcare and transportation. He has co-authored four books and 290+ journal and refereed conference articles. He is a Fellow of the IEEE and AAAS. He is an Associate Editor-in-Chief of the Journal of Parallel and Distributed Computing and an Associate Editor for ACM Computing Surveys, Applied Sciences, Applied Intelligence, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
作者簡介(中文翻譯)
Tania Banerjee, PhD, 是佛羅里達大學計算機與資訊科學工程系的研究助理科學家。她於2012年在佛羅里達大學獲得計算機科學博士學位。她在印度理工學院卡哈爾古爾完成數學碩士學位。她的研究興趣包括視頻分析、智能交通、數據壓縮和高性能計算。
Xiaohui Huang, PhD, 於2020年12月在佛羅里達大學計算機與資訊科學工程系獲得博士學位。她的研究興趣包括機器學習、計算機視覺和智能交通系統。
Aotian Wu目前是佛羅里達大學計算機與資訊科學工程系的博士生。她的研究興趣包括機器學習、計算機視覺和智能交通系統。
Ke Chen目前是佛羅里達大學計算機與資訊科學工程系的博士生。他的研究興趣包括機器學習、計算機架構、操作系統和算法，以及數據結構。
Anand Rangarajan, PhD, 是佛羅里達大學計算機與資訊科學工程系的教授。他的研究興趣包括機器學習、計算機視覺、醫療和高光譜成像，以及意識科學。
Sanjay Ranka, PhD, 是佛羅里達大學計算機資訊科學與工程系的特聘教授。他目前的研究興趣是高性能計算和大數據科學，重點應用於計算流體力學、醫療保健和交通運輸。他共同撰寫了四本書籍和290多篇期刊及經過審核的會議文章。他是IEEE和AAAS的會士，並擔任Journal of Parallel and Distributed Computing的副主編，以及ACM Computing Surveys, Applied Sciences, Applied Intelligence, IEEE/ACM Transactions on Computational Biology and Bioinformatics的副編輯。
