An intelligent video framework for homeland protection

Tu, P., Doretto, G., Krahnstoever, N., Perera, A. G. A., Wheeler, F., Liu, X., Rittscher, J., Sebastian, T., Yu, T., and Harding, K.
An intelligent video framework for homeland protection. Technical Report 2007GRC326, GE Global Research, 2007. Visualization and Computer Vision Laboratory

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Abstract

This paper presents an overview of Intelligent Video work currently under development at the GE Global Research Center and other research institutes. The image formation process is discussed in terms of illumination, methods for automatic camera calibration and lessons learned from machine vision. A variety of approaches for person detection are presented. Crowd segmentation methods enabling the tracking of individuals through dense environments such as retail and mass transit sites are discussed. It is shown how signature generation based on gross appearance can be used to reacquire targets as they leave and enter disjoint fields of view. Camera calibration information is used to further constrain the detection of people and to synthesize a top-view, which fuses all camera views into a composite representation. It is shown how site-wide tracking can be performed in this unified framework. Human faces are an important feature as both a biometric identifier and as a method for determining the focus of attention via head pose estimation. It is shown how automatic pantiltzoom control; active shape/appearance models and super-resolution methods can be used to enhance the face capture and analysis problem. A discussion of additional features that can be used for inferring intent is given. These include body-part motion cues and physiological phenomena such as thermal images of the face.

@TECHREPORT{tuDKPWLRSYH07tr,
  author = {Tu, P. and Doretto, G. and Krahnstoever, N. and Perera, A. G. A.
	and Wheeler, F. and Liu, X. and Rittscher, J. and Sebastian, T. and
	Yu, T. and Harding, K.},
  title = {An intelligent video framework for homeland protection},
  institution = {GE Global Research},
  year = {2007},
  number = {2007GRC326},
  address = {Niskayuna, NY, USA},
  month = apr,
  note = {Visualization and Computer Vision Laboratory},
  bib2html_pubtype = {Tech Reports},
  bib2html_rescat = {Video Surveillance},
  abstract = {This paper presents an overview of Intelligent Video work currently
	under development at the GE Global Research Center and other research
	institutes. The image formation process is discussed in terms of
	illumination, methods for automatic camera calibration and lessons
	learned from machine vision. A variety of approaches for person detection
	are presented. Crowd segmentation methods enabling the tracking of
	individuals through dense environments such as retail and mass transit
	sites are discussed. It is shown how signature generation based on
	gross appearance can be used to reacquire targets as they leave and
	enter disjoint fields of view. Camera calibration information is
	used to further constrain the detection of people and to synthesize
	a top-view, which fuses all camera views into a composite representation.
	It is shown how site-wide tracking can be performed in this unified
	framework. Human faces are an important feature as both a biometric
	identifier and as a method for determining the focus of attention
	via head pose estimation. It is shown how automatic pantiltzoom control;
	active shape/appearance models and super-resolution methods can be
	used to enhance the face capture and analysis problem. A discussion
	of additional features that can be used for inferring intent is given.
	These include body-part motion cues and physiological phenomena such
	as thermal images of the face.},
  file = {tuDKPWLRSYH07tr.pdf:doretto\\report\\tuDKPWLRSYH07tr.pdf:PDF},
  keywords = {intelligent video; surveillance; camera calibration; person detection;
	crowd segmentation; site-wide tracking; person reidentification;
	face modeling; face super-resolution; deception detection},
  owner = {doretto},
  pdf = {doretto\report\tuDKPWLRSYH07tr.pdf},
  timestamp = {2007.04.30}
}

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