Authors:
Sven Tomforde
1
;
Uwe Jänen
1
;
Jörg Hähner
1
and
Martin Hoffmann
2
Affiliations:
1
University of Augsburg, Germany
;
2
Volavis GmbH, Germany
Keyword(s):
Intelligent Surveillance, Cloud Computing, Automated Machine Learning, Smart Camera, Organic Computing
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
Today, high performance and feature rich surveillance systems are very costly as they require an expensive set of infrastructure components. As a consequence, such systems including, e.g., complex automatic video content analysis, are restricted to large scale applications, such as airports or train stations. In smaller settings, e.g. in shop surveillance, mostly low-cost display or record-only systems are in use.
In this position paper we propose to combine two well-known approaches in order to make Intelligent Video Surveillance applicable and affordable in small to medium-scale scenarios. The proposal includes to combine the concept of Smart Cameras, i.e. cameras equipped with local processing resources, with the ideas of Cloud Computing, i.e. the on-demand provisioning of computing and storage services for complex calculations, and the management of large amounts of data, i.e. video storage. The former allows for the cost effective pre-processing of video data close to the sensor
, while using the latter concept does not require large initial investments into expensive infrastructure components such as powerful compute servers.
The paper presents research issues of the necessary system design, including precise system goal and system model aspects. Based on this, we discuss several research issues required to be addressed for solving the overall goals.
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