Accurate silhouettes for surveillance : improved motion segmentation using graph cuts

Chen, Daniel, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2010) Accurate silhouettes for surveillance : improved motion segmentation using graph cuts. In Proceedings of 2010 Digital Image Computing: Techniques and Applications, IEEE, Sydney, Australia, pp. 369-374.

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Silhouettes are common features used by many applications in computer vision. For many of these algorithms to perform optimally, accurately segmenting the objects of interest from the background to extract the silhouettes is essential. Motion segmentation is a popular technique to segment moving objects from the background, however such algorithms can be prone to poor segmentation, particularly in noisy or low contrast conditions. In this paper, the work of [3] combining motion detection with graph cuts, is extended into two novel implementations that aim to allow greater uncertainty in the output of the motion segmentation, providing a less restricted input to the graph cut algorithm. The proposed algorithms are evaluated on a portion of the ETISEO dataset using hand segmented ground truth data, and an improvement in performance over the motion segmentation alone and the baseline system of [3] is shown.

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2 citations in Scopus
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3 citations in Web of Science®

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ID Code: 34248
Item Type: Conference Paper
Refereed: Yes
Keywords: motion segmentation, graph cuts
DOI: 10.1109/DICTA.2010.69
ISBN: 978-0-7695-4271-3
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Copyright Owner: Copyright 2010 Please consult the authors.
Deposited On: 30 Aug 2010 02:12
Last Modified: 27 Feb 2015 00:41

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