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Event prediction in a hybrid camera network

Published: 31 March 2012 Publication History

Abstract

Given a hybrid camera layout—one containing, for example, static and active cameras—and people moving around following established traffic patterns, our goal is to predict a subset of cameras, respective camera parameter settings, and future time windows that will most likely lead to success the vision tasks, such as, face recognition when a camera observes an event of interest. We propose an adaptive probabilistic model that accrues temporal camera correlations over time as the cameras report observed events. No extrinsic, intrinsic, or color calibration of cameras is required. We efficiently obtain the camera parameter predictions using a modified Sequential Monte Carlo method. We demonstrate the performance of the model in an example face detection scenario in both simulated and real environment experiments, using several active cameras.

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cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 8, Issue 2
March 2012
216 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/2140522
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 31 March 2012
Accepted: 01 April 2011
Revised: 01 September 2010
Received: 01 February 2010
Published in TOSN Volume 8, Issue 2

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Author Tags

  1. Video analytics
  2. camera networks
  3. event prediction
  4. sensor networks

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