Roadside Video Data Analysis
Deep Learning
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Beschreibung
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.
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.
Highlights deep learning, to better understand roadside video data segmentation Provides learning techniques based on concepts for roadside video data processing Discusses fire risk assessment based on roadside vegetation biomass estimation Includes supplementary material: sn.pub/extras
Autor*in
Brijesh Verma
Themen in »Roadside Video Data Analysis«
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