VADT 2022: Video Analytics using Digital Twin |
Submission link | https://easychair.org/conferences/?conf=vadt2022 |
Submission Open Date | August 1, 2021 |
Abstract registration deadline | January 27, 2022 |
Submission deadline | April 27, 2022 |
Deep Learning for Video Analytics using Digital Twin (VADT 2022)
The Multimedia data has lot of emerging technology to handle the Business analytics Using Big Multi modal data and AI technique. The modern era has expansion of video data used for modern surveillance and personal data captures. The processing of large video data is indeed a big task. The deep learning based video data analytics is a major platform where most of the researchers focus on the big visual data to modern real time applications. The video data is assumed to be of holding a large spatial and temporal analysis which can be addressed easily with Deep learning to provide the clear pixel level labels with the AI based Deep video data analytics approaches. Besides, deep learning is an approach to solve supervised and unsupervised learning problem to address various issues arising due to GPU clusters. Digital Twin is a emerging topic that focuses mainly on the virtual objects that enhances the retrieval of the big multimedia data. The digital twin may enhance the fast retrieval of multimedia data with the support of deep learning algorithms.AI is a good technique to support the various perspectives with a wide range of capability from analysis to storage with good retrieval. The traditional infrastructures in data centres concentrates on the fast retrieval mechanism, but AI based HPC enables a supercomputing mechanism and flexible access with the support of various machine learning and deep learning algorithms. High Performance Computing (HPC) in association with Artificial intelligence based deep learning is often termed as deep Intelligent HPC, it drives a major shift in the paradigm with data analytics and subsequent data processing.
Objectives and scope:
- To identify Artificial Intelligence techniques in data Analytics and computing environment that are suitable for the Video applications.
- To recognize a wide variety of learning algorithms and how to apply a variety of those algorithms to data.
- To have a good understanding of the fundamental issues and challenges of AI based deep learning: data, model selection, model complexity, etc.
- Integration of heterogeneous computing and big data analytics as a powerful new paradigm to implement the concept of high performance computing in video analytics
- To introduce the advancements in the computing field to effectively handle and make inferences from voluminous and heterogeneous data.
- State-of-the-art AI approaches need to be improved in terms of data integration, interpretability, security and temporal modelling to be effectively applied to the video data
Topics of interest include, but are not limited to:
In this book, we aim to provide a forum for researchers with an interest in efficiency to examine challenging research questions, showcase the state-of-the-art, and share breakthroughs
- Learning data representation from video based on supervised/unsupervised/semi-supervised learning using AI based Digital Twin
- Deep learning on multi-modal social media disadvantage study - Quantitative multi-modal multimedia data analysis using Cloud based Digital Twinning
- Data mining on big multi-modal social media networks using Cloud based AI using distributed analysis
- Social behavior modelling, understanding, and patterns mining with deep models; - Smart cities using Cloud based AI 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.
Editors:
- Vimal Shanmuganathan, Ramco Institute of Technology,Tamilnadu,India
- Subbulakshmi Pasupathi, VIT University- Chennai Campus,Tamilnadu,India
- Seifidine Kadry, Noroff University,ollege,Norway
- Vijayalakshmi, Ramco Institute of Technology,Tamilnadu,India
- Golden Julie, Regional campus ,Anna university,Tamilnadu,India
Important Dates:
- Abstract Submission (Approximately 500 Words): Jan 2022
- Abstract Acceptance Notification: Feb 2022
- Full Chapter Submission: June 2022
- Revision Submission: July 2022
Contact:
All questions about submissions should be emailed to editor.twin@gmail.com (or) svimalphd@gmail.com (or) subbu.psk@gmail.com (or) juliegolden18@gmail.com.