Oral Defense of Doctoral Dissertation – Computational Sciences and Informatics – Automated Monitoring of High Density Crowd Events – Muhammad N. Baqui
Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Sciences and Informatics
Department of Computational and Data Sciences
College of Science
George Mason University
Muhammad N. Baqui
Bachelor of Science, Bangladesh University of Engineering and Technology, 2006
Master of Science, North Dakota State University, 2010
Automated Monitoring of High Density Crowd Events
Tuesday, January 16, 2018, 11:00 a.m.
Research Hall, Room 161
All are invited to attend.
Rainald Löhner, Dissertation Director
Pedestrian traffic is an important subject of surveillance to ensure public safety and traffic management, which may benefit from intelligent and continuous analysis of Pedestrian traffic videos. State-of- the-art methods for intelligent pedestrian traffic surveillance have a number of limitations in automating and computing useful pedestrian traffic information from closed circuit television (CCTV) images. This work aims to automate and augment the traditional pedestrian traffic surveillance system by introducing four components in a novel integrated framework. A fast and efficient particle image velocimetry (PIV) technique is proposed to yield pedestrian velocities for timely management of pedestrian traffic. A machine learning-based regression model, boosted Ferns, is used to improve pedestrian count estimation: an essential metric for high-density pedestrian traffic analysis. A camera perspective model is proposed to improve the speed and position estimate of pedestrian traffic incorporating camera’s intrinsic and extrinsic parameters. All these functional improvements are integrated in a seamless framework to predict future pedestrian traffic distribution, which is a crucial piece of information for pedestrian traffic management. The proposed framework is computationally efficient, suitable for multiple camera feeds with high-density pedestrian traffic videos, and capable of rapidly analyzing and predicting flows of thousands of pedestrians. The proposed framework is one of the first steps towards fully integrated CCTV-based automated pedestrian traffic management system.
A copy of Muhammad’s dissertation is available for examination from Karen Underwood, Department of Computational and Data Sciences, 373 Research Hall. The dissertation is available to read only within the Department and cannot be taken out of the Department or copied.