How Long to Read Video Based Machine Learning for Traffic Intersections

How Long Does it Take to Read Video Based Machine Learning for Traffic Intersections?

It takes the average reader 3 hours and 35 minutes to read Video Based Machine Learning for Traffic Intersections by Tania Banerjee

Assuming a reading speed of 250 words per minute. Learn more

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

How long is Video Based Machine Learning for Traffic Intersections?

Video Based Machine Learning for Traffic Intersections by Tania Banerjee is 213 pages long, and a total of 53,889 words.

This makes it 72% the length of the average book. It also has 66% more words than the average book.

How Long Does it Take to Read Video Based Machine Learning for Traffic Intersections Aloud?

The average oral reading speed is 183 words per minute. This means it takes 4 hours and 54 minutes to read Video Based Machine Learning for Traffic Intersections aloud.

What Reading Level is Video Based Machine Learning for Traffic Intersections?

Video Based Machine Learning for Traffic Intersections is suitable for students ages 12 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

Where Can I Buy Video Based Machine Learning for Traffic Intersections?

Video Based Machine Learning for Traffic Intersections by Tania Banerjee is sold by several retailers and bookshops. However, Read Time works with Amazon to provide an easier way to purchase books.

To buy Video Based Machine Learning for Traffic Intersections by Tania Banerjee on Amazon click the button below.