Recovering background regions in videos of cluttered urban scenes

Iain Rodger, Barry Connor, Neil M. Robertson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper we present a novel segmentation technique and adapt it for enhanced background recovery in crowded urban scenes. Building on an initial superpixels representation, smaller regions are merged depending on their perceived similarity to develop larger regions. Based on the observation that human activity tends to be quite structured, during this process we exploit emerging foreground context (tracks of people) to influence the segmentation process via Bayesian priors. These priors incorporate both temporal and spatial smoothing. We validate the approach on benchmarked urban datasets and show that our method improves on established image segmentation methods.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP)
Subtitle of host publicationProceedings
PublisherIEEE
Pages4097-4101
Number of pages5
ISBN (Print)9781479983391
DOIs
Publication statusPublished - 9 Dec 2015
Event22nd IEEE International Conference on Image Processing 2015 - Quebec City, Canada
Duration: 27 Sep 201530 Sep 2015
Conference number: 22

Conference

Conference22nd IEEE International Conference on Image Processing 2015
Abbreviated titleICIP 2015
CountryCanada
CityQuebec City
Period27/09/1530/09/15

Keywords

  • Foreground Context
  • Image Segmentation
  • Pattern Recognition
  • Superpixels

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Rodger, I., Connor, B., & Robertson, N. M. (2015). Recovering background regions in videos of cluttered urban scenes. In 2015 IEEE International Conference on Image Processing (ICIP): Proceedings (pp. 4097-4101). [7351576] IEEE. https://doi.org/10.1109/ICIP.2015.7351576