A saliency model for active camera control

Kim, S. J., Doretto, G., Rittscher, J., Tu, P., and Pollefeys, M.
A saliency model for active camera control. Technical Report 2005GRC591, GE Global Research, 2005. Visualization and Computer Vision Laboratory

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Abstract

The goal of this work is to provide information for the automatic control of an active camera. One way to achieve this is to run a set of saliency detectors in the scene which is tuned to a specific class of objects. As opposed to this, we propose a formal definition for what we mean for salient motion by means of the concept of stationarity of stochastic processes. We propose a hierarchy of generative models for salient and nuisance motion, along with recursive learning procedures for on-line processing. By doing so, we turn the problem of detecting salient motion into the problem of model change detection, which we solve optimally online using the sequential generalized likelihood ratio test. Our system is designed for realtime applications, and is able to detect salient motion even in severely cluttered scenes.

@TECHREPORT{kimDRTP05tr,
  author = {Kim, S. J. and Doretto, G. and Rittscher, J. and Tu, P. and Pollefeys,
	M.},
  title = {A saliency model for active camera control},
  institution = {GE Global Research},
  year = {2005},
  number = {2005GRC591},
  address = {Niskayuna, NY, USA},
  month = nov,
  note = {Visualization and Computer Vision Laboratory},
  bib2html_pubtype = {Tech Reports},
  bib2html_rescat = {Video Surveillance, Dynamic Textures, Visual Motion Detection},
  abstract = {The goal of this work is to provide information for the automatic
	control of an active camera. One way to achieve this is to run a
	set of saliency detectors in the scene which is tuned to a specific
	class of objects. As opposed to this, we propose a formal definition
	for what we mean for salient motion by means of the concept of stationarity
	of stochastic processes. We propose a hierarchy of generative models
	for salient and nuisance motion, along with recursive learning procedures
	for on-line processing. By doing so, we turn the problem of detecting
	salient motion into the problem of model change detection, which
	we solve optimally online using the sequential generalized likelihood
	ratio test. Our system is designed for realtime applications, and
	is able to detect salient motion even in severely cluttered scenes.},
  file = {kimDRTP05tr.pdf:doretto\\report\\kimDRTP05tr.pdf:PDF},
  keywords = {salient activity detection, linear dynamic systems, motion detection,
	pan tilt zoom, camera, foreground/background segmentation, activity/novelty
	detection, dynamic textures, sequential likelihood ratio},
  owner = {doretto},
  pdf = {doretto\report\kimDRTP05tr.pdf},
  timestamp = {2006.11.29}
}

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