Article in Proceedings INPROC-2013-62

BibliographyHong, Kirak; Ottenwälder, Beate; Ramachandran, Umakishore: Scalable Spatio-temporal Analysis on Distributed Camera Networks.
In: Proceedings of the 7th International Symposium on Intelligent Distributed Computing (IDC 2013).
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
Studies in Computational Intelligence; 511, pp. 131-140, english.
Springer International Publishing, September 2013.
DOI: 10.1007/978-3-319-01571-2_16.
Article in Proceedings (Conference Paper).
CR-SchemaC.2.4 (Distributed Systems)

Technological advances and the low cost of sensors enable the deployment of large-scale camera networks in airports and metropolises. A well-known technique, called spatio-temporal analysis, enables detecting anomalies such as an individual entering into a restricted area without permission. Spatio-temporal analysis requires a large amount of system resources to infer locations of occupants in real-time. In particular, state update becomes a bottleneck due to computation and communication overhead to update possibly large application state. In this paper we propose a system design and mechanisms for scalable spatio-temporal analysis. We present a distributed system architecture including smart cameras and distributed worker nodes in the cloud to enable real-time spatio-temporal analysis on large-scale camera networks. Furthermore we propose and implement a couple of selective update mechanisms to further improve scalability of our system by reducing the communication cost for state update.

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Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems
Entry dateMay 5, 2014
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