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Proceedings Paper

Exploiting semantics for scheduling real-time data collection from sensors to maximize event detection
Author(s): Ronen Vaisenberg; Sharad Mehrotra; Deva Ramanan
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Paper Abstract

A distributed camera network allows for many compelling applications such as large-scale tracking or event detection. In most practical systems, resources are constrained. Although one would like to probe every camera at every time instant and store every frame, this is simply not feasible. Constraints arise from network bandwidth restrictions, I/O and disk usage from writing images, and CPU usage needed to extract features from the images. Assume that, due to resource constraints, only a subset of sensors can be probed at any given time unit. This paper examines the problem of selecting the "best" subset of sensors to probe under some user-specified objective - e.g., detecting as much motion as possible. With this objective, we would like to probe a camera when we expect motion, but would not like to waste resources on a non-active camera. The main idea behind our approach is the use of sensor semantics to guide the scheduling of resources. We learn a dynamic probabilistic model of motion correlations between cameras, and use the model to guide resource allocation for our sensor network. Although previous work has leveraged probabilistic models for sensor-scheduling, our work is distinct in its focus on real-time building-monitoring using a camera network. We validate our approach on a sensor network of a dozen cameras spread throughout a university building, recording measurements of unscripted human activity over a two week period. We automatically learnt a semantic model of typical behaviors, and show that one can significantly improve effciency of resource allocation by exploiting this model.

Paper Details

Date Published: 19 January 2009
PDF: 13 pages
Proc. SPIE 7253, Multimedia Computing and Networking 2009, 72530B (19 January 2009);
Show Author Affiliations
Ronen Vaisenberg, Univ. of California, Irvine (United States)
Sharad Mehrotra, Univ. of California, Irvine (United States)
Deva Ramanan, Univ. of California, Irvine (United States)

Published in SPIE Proceedings Vol. 7253:
Multimedia Computing and Networking 2009
Reza Rejaie; Ketan D. Mayer-Patel, Editor(s)

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