Roadside Video Data Analysis

<br>1 Introduction<br>Background<br>Collection of Roadside Video Data<br>Industry Data<br>Benchmark Data<br>Applications Using Roadside Video Data<br>Outline of the Book<br><br>2 Roadside Video Data Analysis Framework<br>Overview<br>Methodology<br>Preprocessing of Roadside Video Data<br>Segmentation of Roadside Video Data into Objects<br>Vegetation, Roads, Signs, Sky <br>Feature Extraction from Objects <br>Classification of Roadside Objects<br>Applications of Classified Roadside Objects<br>Algorithms and Pseudocodes<br><br>3 Learning and Impact on Roadside Video Data Analysis<br>Neural Network Learning<br>Support Vector Machine Learning <br>K-Nearest Neighbor Learning <br>Cluster Learning<br>Hierarchical Learning <br>Fuzzy C-Means Learning <br>Region Merging Learning <br>Probabilistic Learning <br>Ensemble Learning<br>Deep Learning<br><br>4 Applications in Roadside Fire Risk Assessment<br>Scene Labeling<br>Roadside Vegetation Classification<br>Vegetation Biomass Estimation<br><br>5 Conclusions and Future Insights<br>Recommendations<br>New Challenges<br>New Opportunities and Applications<br><br>