Unified crowd segmentation

Tu, P., Sebastian, T., Doretto, G., Krahnstoever, N., Rittscher, J., and Yu, T.
Unified crowd segmentation. In Proceedings of European Conference on Computer Vision, 2008.

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Abstract

This paper presents a unified approach to crowd segmentation. A global solution is generated using an Expectation Maximization framework. Initially, a head and shoulder detector is used to nominate an exhaustive set of person locations and these form the person hypotheses. The image is then partitioned into a grid of small patches which are each assigned to one of the person hypotheses. A key idea of this paper is that while whole body monolithic person detectors can fail due to occlusion, a partial response to such a detector can be used to evaluate the likelihood of a single patch being assigned to a hypothesis. This captures local appearance information without having to learn specific appearance models. The likelihood of a pair of patches being assigned to a person hypothesis is evaluated based on low level image features such as uniform motion fields and color constancy. During the E-step, the single and pairwise likelihoods are used to compute a globally optimal set of assignments of patches to hypotheses. In the M-step, parameters which enforce global consistency of assignments are estimated. This can be viewed as a form of occlusion reasoning. The final assignment of patches to hypotheses constitutes a segmentation of the crowd. The resulting system provides a global solution that does not require background modeling and is robust with respect to clutter and partial occlusion.

@INPROCEEDINGS{tuSDKRY08eccv,
  author = {Tu, P. and Sebastian, T. and Doretto, G. and Krahnstoever, N. and
	Rittscher, J. and Yu, T.},
  title = {Unified crowd segmentation},
  booktitle = eccv,
  year = {2008},
  note = {\btohremove{\textsf{\textbf{AR: XX.X\%}}}},
  bib2html_pubtype = {Refereed Conferences},
  bib2html_rescat = {Video Surveillance, People Detection, Integral Image Computations,
	People Tracking},
  abstract = {This paper presents a unified approach to crowd segmentation. A global
	solution is generated using an Expectation Maximization framework.
	Initially, a head and shoulder detector is used to nominate an exhaustive
	set of person locations and these form the person hypotheses. The
	image is then partitioned into a grid of small patches which are
	each assigned to one of the person hypotheses. A key idea of this
	paper is that while whole body monolithic person detectors can fail
	due to occlusion, a partial response to such a detector can be used
	to evaluate the likelihood of a single patch being assigned to a
	hypothesis. This captures local appearance information without having
	to learn specific appearance models. The likelihood of a pair of
	patches being assigned to a person hypothesis is evaluated based
	on low level image features such as uniform motion fields and color
	constancy. During the E-step, the single and pairwise likelihoods
	are used to compute a globally optimal set of assignments of patches
	to hypotheses. In the M-step, parameters which enforce global consistency
	of assignments are estimated. This can be viewed as a form of occlusion
	reasoning. The final assignment of patches to hypotheses constitutes
	a segmentation of the crowd. The resulting system provides a global
	solution that does not require background modeling and is robust
	with respect to clutter and partial occlusion.},
  file = {tuSDKRY08eccv.pdf:doretto\\conference\\tuSDKRY08eccv.pdf:PDF},
  owner = {doretto},
  timestamp = {2008.01.16}
}

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