Deep Learning for Video Analytics using Digital Twin: (River Publishers Series in Information Science and Technology)

Over the recent years, a considerable amount of effort has been devoted, both in industry and academia on multimedia data handling. There is emerging technology for handling business analytics, using big multi modal data and AI techniques. There has been an expansion of video data used for modern surveillance and personal data captures. The processing of such large amounts of video data is a huge task. Deep learning based video data analytics is a major platform where most researchers focus on big visual data with modern real time applications. Video data is assumed to be needing a large spatial and temporal analysis which can be addressed easily with Deep Learning to provide the clear pixel level labels with AI based Deep video data analytics approaches. Also, Deep Learning is a useful approach to solve supervised and unsupervised learning problems and to address various issues arising due to GPU clusters. This volume provides a forum for researchers, especially those with an interest in efficiency, to examine challenging research questions, showcase state-of-the-art, and share breakthroughs in Multimedia Data Handling using Digital Twin technology. Technical topics discussed in the book include: • Learning data representation from video based on supervised/unsupervised/semi-supervised learning • Deep Learning on multi-modal social media disadvantages • Data mining on big multi-modal social media networks using distributed analysis • Social behavior modelling, understanding, and pattern mining with deep models using Digital Twin • IoT based Video Analytics using Cloud based AI using distributed analysis • Web video understanding using deep learning techniques, including classification, annotation, event detection and recognition, authoring and editing using Cloud based AI • Video highlights, summary and storyboard generation using Cloud based AI using distributed analysis • Digital Twin based Segmentation and tracking using Cloud based AI using distributed analysis • Data collections, benchmarking, and performance evaluation with Cloud based AI using distributed analysis • Human behavior analysis in real-time surveillance video surveillance using Cloud based AI

Table of Contents:
Preface; Participants of the Reviewing Process; 1 Learning data representation from video based on supervised/unsupervised/semi-supervised learning; 2. Deep learning on multi-modal social media disadvantage study - Quantitative multi-modal multimedia data analysis ; 3. Data mining on big multi-modal social media networks; 4. Social behavior modelling, understanding, and patterns mining with deep models; 5. IoT based Video Analytics using Cloud;6. Web video understanding using deep learning techniques,;7. Video highlights, summary and storyboard generation using Cloud.; 8. Digital Twin based Segmentation and tracking using Cloud .; 9. Data collections, benchmarking, and performance evaluation with Cloud.; 10. Human behavior analysis in real-time surveillance video surveillance.; Author Index; Keyword Index.

About the Author :
Dr.S.Vimal is working in Department of CSE, Ramco Institute of Technology, India. He has acted as Session chairs, organizing committee member, advisory committee and outreach committee member in various international conferences in various prestigious Conferences. His areas of interest include Game Modelling, Artificial Intelligence, Cognitive radio networks, Network security, Machine Learning and Big data Analytics. He has served as Guest editor for around 20 SCI journals and editored 2 books in scopus indexed. Dr. Seifedine Kadry has a Bachelor degree in applied mathematics in 1999 from Lebanese University, MS degree in computation in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in engineering science in 2017 from Rouen University. At present his research focuses on education using technology, smart cities, system prognostics, stochastic systems, and probability and reliability analysis. He is a Fellow of IET, Fellow of ACSIT and ABET program evaluator. Fellow Member in IET. Dr. K.Vijayalakshmi works as professor and the Head of Department of Computer Science and Engineering in Ramco Institute of Technology. She has more than 21 years of teaching experience in engineering colleges and guiding research scholars under Anna University Chennai. She has had many of her national and international journals published by Elsevier and Springer. Her area of interest include network security and optimization. Dr.P.Subbulakshmi received her B.E degree in Computer Science and Eng in 2008 from Anna University Chennai and ME degree in Computer Science and Engineering in 2011 from Anna University Chennai. Currently she completed her Ph.D from Anna University Chennai 2019. Chennai, Tamil Nadu, India. She has around 10 years of teaching experience. She has published several papers in SCI, Scopus indexed Journals, Conferences. She has done various online courses in NPTEL and organised funded seminars too. Her modest areas of interest include Cognitive Radio Networks, Computer networks, Cryptography, Wireless Networks & security, Big data analytics, Game theory and Machine Learning. She has mentored various MHRD certifications and institutional expert member in Outcome Based Education-NBA Golden Julie received her B.E degree in Computer Science and Engg in 2005 from Anna University Chennai and ME degree in Computer Science and Engineering in 2008 from Anna University Chennai. Currently she completed her Ph.D from Anna University Chennai 2017. Presently she is working as assistant professor in Regional centre Anna university, Tirunelveli, India She has published many research papers in various fields. Her research area includes Wireless Sensor Ad Hoc Networks Soft Computing, Internet of Things and Image Processing. She is a member of ISTE.