Roadside Video Data Analysis
-15%
portes grátis
Roadside Video Data Analysis
Deep Learning
Verma, Brijesh; Stockwell, David
Springer Verlag, Singapore
05/2017
189
Dura
Inglês
9789811045387
15 a 20 dias
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.
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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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.
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
