Roadside Video Data Analysis Deep Learning [Hardcover]

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.

1 Introduction
Background
Collection of Roadside Video Data
Industry Data
Benchmark Data
Applications Using Roadside Video Data
Outline of the Book

2 Roadside Video Data Analysis Framework
Overview
Methodology
Preprocessing of Roadside Video Data
Segmentation of Roadside Video Data into Objects
Vegetation, Roads, Signs, Sky
Feature Extraction from Objects
Classification of Roadside Objects
Applications of Classified Roadside Objects
Algorithms and Pseudocodes

3 Learning and Impact on Roadside Video Data Analysis
Neural Network Learning
Support Vector Machine Learning
K-Nearest Neighbor Learning
Cluster Learning
Hierarchical Learning
Fuzzy C-Means Learning
Region Merging Learning
Probabilistic Learning
Ensemble Learning
Deep Learning

4 Applications in Roadside Fire Risk Assessment
Scene Labeling
Roadside Vegetation Classification
Vegetation Biomass Estimation

5 Conclusions and Future Insights
Recommendations
New Challenges
New Opportunities and Applications