Feature-based object modelling for visual surveillance

Abstract

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 first 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.