Saleem Durai M-A

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Sreenu G

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Journal of Big Data

The ever-expanding urbanization and the advent of smart cities need better crowd management and security surveillance systems. Advanced systems are required to improve and automate the crowd management system. The aim of the closed circuit television and visual monitoring systems using multiple cameras faces many challenges like illumination variance, occlusion and small spatial–temporal resolution, person in sleep, shadows, dynamic backgrounds, and noises. Therefore, the crowd monitoring, prevention of stampedes and crowd-related emergencies in the smart cities are major challenging problems. In this paper, we propose an intelligent decision computing based paradigm for crowd monitoring in the smart city. In the intelligent computing based framework, the optimization algorithm is applied to compute the feature of crowd motion and measure the correlation between agents based motion model and the crowd data using extended Kalman filtering approach and KL-divergence technique. The proposed framework measures the correlation measure based on extracted novel distinctive feature, and holistic feature of crowd data represent and to classify the crowd motion of individual. Our experimental results demonstrate that the proposed approach yields 96.20% average precision in classifying real-world highly dense crowd scenes. © 2017 Elsevier Inc.

Journal of Parallel and Distributed Computing

An intelligent decision computing paradigm for crowd monitoring in the smart city

Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities

Neurocomputing

Jointly learning perceptually heterogeneous features for blind 3D video quality assessment

Pattern Recognition Letters

Boosting deep attribute learning via support vector regression for fast moving crowd counting

D-STC: Deep learning with spatio-temporal constraints for train drivers detection from videos

Intelligent video surveillance: a review through deep learning techniques for crowd analysis

Journal	Data powered by TypesetJournal of Big Data
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
Open Access	Yes