Moving object segmentation using scene understanding

Perera, A. G. A., Brooksby, G., Hoogs, A., and Doretto, G.
Moving object segmentation using scene understanding. In Proceedings of IEEE Computer Society Workshop on Perceptual Organization in Computer Vision, pp. 201–208, New York City, NY, USA, June 17--22, 2006.

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Abstract

We present a novel approach to moving object detection in video taken from a translating, rotating and zooming sensor, with a focus on detecting very small objects in as few frames as possible. The primary innovation is to incorporate automatically computed scene understanding of the video directly into the motion segmentation process. Scene understanding provides spatial and semantic context that is used to improve frame-to-frame homography computation, as well as direct reduction of false alarms. The method can be applied to virtually any motion segmentation algorithm, and we explore its utility for three: frame differencing, tensor voting, and generalized PCA. The approach is especially effective on sequences with large scene depth and much parallax, as often occurs when the sensor is close to the scene. In one difficult sequence, our results show an 8-fold reduction of false positives on average, with essentially no impact on the true positive rate. We also show how scene understanding can be used to increase the accuracy of frame-to-frame homography estimates.

@INPROCEEDINGS{pereraBHD06pocv,
  author = {Perera, A. G. A. and Brooksby, G. and Hoogs, A. and Doretto, G.},
  title = {Moving object segmentation using scene understanding},
  booktitle = {Proceedings of IEEE Computer Society Workshop on Perceptual Organization
	in Computer Vision},
  year = {2006},
  pages = {201--208},
  address = {New York City, NY, USA},
  month = {June 17--22,},
  note = {\btohremove{\textsf{\textbf{GSCC: 1}}}},
  bib2html_pubtype = {Refereed Conferences},
  bib2html_rescat = {Video Surveillance, Visual Motion Segmentation},
  abstract = {We present a novel approach to moving object detection in video taken
	from a translating, rotating and zooming sensor, with a focus on
	detecting very small objects in as few frames as possible. The primary
	innovation is to incorporate automatically computed scene understanding
	of the video directly into the motion segmentation process. Scene
	understanding provides spatial and semantic context that is used
	to improve frame-to-frame homography computation, as well as direct
	reduction of false alarms. The method can be applied to virtually
	any motion segmentation algorithm, and we explore its utility for
	three: frame differencing, tensor voting, and generalized PCA. The
	approach is especially effective on sequences with large scene depth
	and much parallax, as often occurs when the sensor is close to the
	scene. In one difficult sequence, our results show an 8-fold reduction
	of false positives on average, with essentially no impact on the
	true positive rate. We also show how scene understanding can be used
	to increase the accuracy of frame-to-frame homography estimates.},
  doi = {10.1109/CVPRW.2006.132},
  file = {pereraBHD06pocv.pdf:doretto\\conference\\pereraBHD06pocv.pdf:PDF},
  owner = {doretto},
  pdf = {doretto\conference\pereraBHD06pocv.pdf},
  timestamp = {2006.11.29}
}

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