Roadside Video Data Analysis (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
189
Utgivningsdatum
2018-12-12
Upplaga
Softcover reprint of the original 1st ed. 2017
Förlag
Springer Verlag, Singapore
Medarbetare
Zhang, Ligang / Stockwell, David
Illustrationer
75 Tables, color; 68 Illustrations, color; 11 Illustrations, black and white; XXV, 189 p. 79 illus.,
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9789811351624
Roadside Video Data Analysis (häftad)

Roadside Video Data Analysis

Deep Learning

Häftad Engelska, 2018-12-12
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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. 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.
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Övrig information

Brijesh Verma is a Professor and the Director of the Centre for Intelligent Systems at Central Queensland University, Brisbane, Australia. His main research interests include computational intelligence and pattern recognition. He has published a number of books and book chapters and over one hundred fifty papers in journals and conference proceedings. He has served on the editorial boards of six international journals including Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, Associate Editor for IEEE Transactions on Biomedicine in Information Technology and Editor-in-Chief for International Journal of Computational Intelligence & Applications. He has served on the organising and program committees of over thirty international conferences including IEEE International Joint Conference on Neural Networks (IJCNN) and IEEE Congress on Evolutionary Computation (CEC). He was the IJCNN Special Sessions Chair for 2012 IEEE World Congress on Computational Intelligence (WCCI). He was a Chair of a Special Session on Computational Intelligence based Ensemble Classifiers at IEEE IJCNN 2013 and a Chair of a Special Session on Machine Learning for Computer Vision at IEEE IJCNN 2014 and IEEE WCCI 2016. He is a Co-Chair of Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition at IEEE SSCI 2017. He has served as the Chair of the IEEE Computational Intelligence Society's Queensland Chapter in 2007-2008 and won the outstanding chapter award in 2009. He has also served on IEEE CIS senior members' program subcommittee (2011-2012), IEEE CIS outstanding chapter award subcommittee (2009-2011) and IEEE CIS representative on IEEE Nanotechnology Council (2014-2015). Ligang Zhang is a Research Fellow in the School of Engineering and Technology at Central Queensland University, Australia. His research interests include image segmentation and recognition, facial expression recognition, affective computing and machine learning. He has published more than 30 papers in journals and conference proceedings. David Stockwell is an Adjunct Research Fellow at Central Queensland University and an Environmental Officer in the Queensland Department of Transport and Main Roads, Australia. He has a strong background in environmental data modelling and his research interests include statistical analysis, machine learning and pattern recognition. He has worked as a postdoctoral research fellow at the San Diego Supercomputer Center, University of California in USA. He has widely published and has over 4400 citations in Google scholar.

Innehållsförteckning

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