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Feature-based object modelling for visual surveillance

9 years 5 months ago
Feature-based object modelling for visual surveillance
This paper introduces a new feature-based technique for implicitly modelling objects in visual surveillance. Previous work has generally employed background subtraction and other image or motion based object segmentation schemes for the rst step in identifying objects worthy of attention. Given that background subtraction is a notoriously noisy process, this paper investigates an alternative strategy by instead employing feature (SIFT [1]) clustering to characterise objects. The segmentation step is therefore performed on the sparse feature space instead of the image data itself. The paper also presents an application employing this idea for automatic detection of illegal dumping from CCTV footage. The Viterbi algorithm then allows robust tracking [2] of objects generated from the spatial clustering of these sparse foreground feature maps.
Gary Baugh, Anil C. Kokaram
Added 20 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2008
Where ICIP
Authors Gary Baugh, Anil C. Kokaram
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