Roadside Video Data Analysis

Deep Learning

  • Brijesh Verma
  • Ligang Zhang
  • David Stockwell

Part of the Studies in Computational Intelligence book series (SCI, volume 711)

Table of contents

  1. Front Matter
    Pages i-xxv
  2. Brijesh Verma, Ligang Zhang, David Stockwell
    Pages 1-12
  3. Brijesh Verma, Ligang Zhang, David Stockwell
    Pages 13-39
  4. Brijesh Verma, Ligang Zhang, David Stockwell
    Pages 41-118
  5. Brijesh Verma, Ligang Zhang, David Stockwell
    Pages 119-157
  6. Brijesh Verma, Ligang Zhang, David Stockwell
    Pages 159-183
  7. Brijesh Verma, Ligang Zhang, David Stockwell
    Pages 185-189

About this book

Introduction

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.

Keywords

Feature extraction Classified roadside objects Roadside Fire Risk Assessment Neural Network Learning Support Vector Machine Learning K-Nearest Neighbor Learning Scene labeling Cluster Learning Vegetation biomass estimation Hierarchical Learning Fuzzy C-Means Learning Probabilistic Learning Ensemble Learning

Authors and affiliations

  • Brijesh Verma
    • 1
  • Ligang Zhang
    • 2
  • David Stockwell
    • 3
  1. 1.School of Engineering and TechnologyCentral Queensland UniversityBrisbaneAustralia
  2. 2.School of Engineering and TechnologyCentral Queensland UniversityBrisbaneAustralia
  3. 3.School of Engineering and TechnologyCentral Queensland UniversityBrisbaneAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-4539-4
  • Copyright Information Springer Nature Singapore Pte Ltd. 2017
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering
  • Print ISBN 978-981-10-4538-7
  • Online ISBN 978-981-10-4539-4
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book