Towards Autonomous Video Surveillance
It is estimated that in 2014 there were over 100 million surveillance cameras in the world. Fueled by security concerns, this number continues to steadily grow. As monitoring of video feeds by human operators is not scalable, automatic surveillance tools are needed. In this talk, I will cover a complete video surveillance processing chain, developed over years at Boston University, from low-level video analysis to summarization of dynamic events. I will focus on three fundamental questions posed in video surveillance: ``How to detect anomalous events in a visual scene? How to classify those events? How to represent them succinctly?’’ First, I will present ``behavior subtraction’’, an extension of ``background subtraction’’ to scenes with dynamic backgrounds (e.g., water surface that is notoriously difficult to handle), which can detect complex anomalies in surveillance video. Then, in order to classify activities within the detected anomalies, I will discuss activity recognition on covariance manifolds. Finally, I will describe ``video condensation’’, a computational method to succinctly summarize activities of interest for efficient evaluation by human operators.
Date and Time
Location
Hosts
Registration
- Date: 22 May 2019
- Time: 10:15 AM UTC to 11:15 AM UTC
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- Nowowiejska 15/19
- Warszawa, Mazowieckie
- Poland 00-665
- Building: Wydział Elektroniki i Technik Informacyjnych
- Room Number: 229
Speakers
Prof. Janusz Konrad of Boston University
Towards Autonomous Video Surveillance
It is estimated that in 2014 there were over 100 million surveillance cameras in the world. Fueled by security concerns, this number continues to steadily grow. As monitoring of video feeds by human operators is not scalable, automatic surveillance tools are needed. In this talk, I will cover a complete video surveillance processing chain, developed over years at Boston University, from low-level video analysis to summarization of dynamic events. I will focus on three fundamental questions posed in video surveillance: ``How to detect anomalous events in a visual scene? How to classify those events? How to represent them succinctly?’’ First, I will present ``behavior subtraction’’, an extension of ``background subtraction’’ to scenes with dynamic backgrounds (e.g., water surface that is notoriously difficult to handle), which can detect complex anomalies in surveillance video. Then, in order to classify activities within the detected anomalies, I will discuss activity recognition on covariance manifolds. Finally, I will describe ``video condensation’’, a computational method to succinctly summarize activities of interest for efficient evaluation by human operators.
Biography:
Janusz Konrad received Master’s degree from Technical University of Szczecin, Poland in 1980 and PhD degree from McGill University, Montréal, Canada in 1984. He joined INRS-Télécommunications, Montréal as a post-doctoral fellow and, since 1992, as a faculty member. Since 2000, he has been on faculty at Boston University. He is an IEEE Fellow and a recipient of several IEEE and EURASIP Best Paper awards. He has been actively engaged in the IEEE Signal Processing Society as a member of various boards and technical committees, as well as an organizer of conferences. He has also been on editorial boards of various EURASIP journals. His research interests include video processing and computer vision, stereoscopic and 3-D imaging and displays, visual sensor networks, human-computer interfaces, and cybersecurity
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