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Proceedings Paper

Exploiting semantics for sensor re-calibration in event detection systems
Author(s): Ronen Vaisenberg; Shengyue Ji; Bijit Hore; Sharad Mehrotra; Nalini Venkatasubramanian
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Paper Abstract

Event detection from a video stream is becoming an important and challenging task in surveillance and sentient systems. While computer vision has been extensively studied to solve different kinds of detection problems over time, it is still a hard problem and even in a controlled environment only simple events can be detected with a high degree of accuracy. Instead of struggling to improve event detection using image processing only, we bring in semantics to direct traditional image processing. Semantics are the underlying facts that hide beneath video frames, which can not be "seen" directly by image processing. In this work we demonstrate that time sequence semantics can be exploited to guide unsupervised re-calibration of the event detection system. We present an instantiation of our ideas by using an appliance as an example--Coffee Pot level detection based on video data--to show that semantics can guide the re-calibration of the detection model. This work exploits time sequence semantics to detect when re-calibration is required to automatically relearn a new detection model for the newly evolved system state and to resume monitoring with a higher rate of accuracy.

Paper Details

Date Published: 28 January 2008
PDF: 12 pages
Proc. SPIE 6818, Multimedia Computing and Networking 2008, 68180P (28 January 2008); doi: 10.1117/12.774906
Show Author Affiliations
Ronen Vaisenberg, Univ. of California, Irvine (United States)
Shengyue Ji, Univ. of California, Irvine (United States)
Bijit Hore, Univ. of California, Irvine (United States)
Sharad Mehrotra, Univ. of California, Irvine (United States)
Nalini Venkatasubramanian, Univ. of California, Irvine (United States)


Published in SPIE Proceedings Vol. 6818:
Multimedia Computing and Networking 2008
Reza Rejaie; Roger Zimmermann, Editor(s)

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