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2017 | Book

Introduction Read chapter Read first chapter

Roadside Video Data Analysis

Deep Learning

Authors: Brijesh Verma, Ligang Zhang, David Stockwell

Publisher: Springer Singapore

Book Series : Studies in Computational Intelligence

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book highlights the methods and applications for roadside video data analysis, with a particular focus on the use of deep learning to solve roadside video data segmentation and classification problems. It describes system architectures and methodologies that are specifically built upon learning concepts for roadside video data processing, and offers a detailed analysis of the segmentation, feature extraction and classification processes. Lastly, it demonstrates the applications of roadside video data analysis including scene labelling, roadside vegetation classification and vegetation biomass estimation in fire risk assessment.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
This chapter presents brief background information and datasets for roadside video data analysis.
Brijesh Verma, Ligang Zhang, David Stockwell
Chapter 2. Roadside Video Data Analysis Framework
Abstract
This chapter introduces a general framework for roadside video data analysis. The main processing steps in the framework are described separately.
Brijesh Verma, Ligang Zhang, David Stockwell
Chapter 3. Non-deep Learning Techniques for Roadside Video Data Analysis
Abstract
In this chapter, we describe traditional non-deep learning approaches that are used for roadside video data analysis. Each type of these learning approaches is described separately in each section, which primarily focuses on related prior work, technical details of each approach, experimental design, and performance analysis. We also give a short summary of each learning approach at the end of each section.
Brijesh Verma, Ligang Zhang, David Stockwell
Chapter 4. Deep Learning Techniques for Roadside Video Data Analysis
Abstract
In this chapter, we describe deep learning techniques that are proposed for roadside video data analysis. We firstly present an introduction to deep learning concepts, and a short review of several typical types of CNN.
Brijesh Verma, Ligang Zhang, David Stockwell
Chapter 5. Case Study: Roadside Video Data Analysis for Fire Risk Assessment
Abstract
In this chapter, we present a case study of utilizing machine learning techniques for fire risk assessments on roadside video data.
Brijesh Verma, Ligang Zhang, David Stockwell
Chapter 6. Conclusion and Future Insight
Abstract
In this chapter, we present several recommendations for future research work based on the experimental results obtained using various non-deep and deep learning techniques.
Brijesh Verma, Ligang Zhang, David Stockwell
Metadata
Title
Roadside Video Data Analysis
Authors
Brijesh Verma
Ligang Zhang
David Stockwell
Copyright Year
2017
Publisher
Springer Singapore
Electronic ISBN
978-981-10-4539-4
Print ISBN
978-981-10-4538-7
DOI
https://doi.org/10.1007/978-981-10-4539-4