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.
Roadside Video Data Analysis: Deep Learning
Table of contents
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
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