Video based machine learning for traffic intersections (Record no. 9075)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03687nam a22002657a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250409164737.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250409b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781032542263 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3 |
| Item number | BAN |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Banerjee, Tania |
| 245 ## - TITLE STATEMENT | |
| Title | Video based machine learning for traffic intersections |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Routledge |
| Place of publication, distribution, etc. | New York |
| Date of publication, distribution, etc. | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxvi, 167 p. |
| 365 ## - TRADE PRICE | |
| Price type code | GBP |
| Price amount | 110.00 |
| 500 ## - GENERAL NOTE | |
| General note | Table of contents:<br/>1. Introduction 2. Detection, Tracking, and Classification 3. Near-miss Detection 4. Severe Events 5. Performance-Safety Trade-offs 6. Trajectory Prediction 7. Vehicle Tracking across Multiple Intersections 8. User Interface 9. Conclusion<br/><br/>(https://www.routledge.com/Video-Based-Machine-Learning-for-Traffic-Intersections/Banerjee-Huang-Wu-Chen-Rangarajan-Ranka/p/book/9781032542263?srsltid=AfmBOooYRA-l5jj_kpYwFD6j4aPGCwypQ9cRnpyumWsSdSQVDtMBxhip) |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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.<br/><br/>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.<br/><br/>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.<br/><br/>Key Features:<br/><br/>Describes the development and challenges associated with Intelligent Transportation Systems (ITS)<br/>Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection<br/>Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts<br/><br/>(https://www.routledge.com/Video-Based-Machine-Learning-for-Traffic-Intersections/Banerjee-Huang-Wu-Chen-Rangarajan-Ranka/p/book/9781032542263?srsltid=AfmBOooYRA-l5jj_kpYwFD6j4aPGCwypQ9cRnpyumWsSdSQVDtMBxhip) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Traffic signals |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer vision |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Huang, Xiaohui |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Wu, Aotian |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ke, Chen |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Rangarajan, Anand |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ranka, Sanjay |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Book |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Bill No | Bill Date | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Accession Number | Date last seen | Copy number | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | 1189152 | 11-03-2025 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 03/20/2025 | Atlantic Publishers & Distributors | 7957.95 | 006.3 BAN | 007959 | 03/20/2025 | 1 | 12243.00 | 03/20/2025 | Book |