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Title:Semi-supervised Learning for Anomalous Trajectory Detection
Authors: Rowland Sillito ; Robert Fisher
Date:Sep 2008
Publication Title:Proc. British Machine Vision Conference (BMVC)
Publication Type:Conference Paper Publication Status:Published
Page Nos:1035-1044
ISBN/ISSN:978 1 901725 36 0
A novel learning framework is proposed for anomalous behaviour detection in a video surveillance scenario, so that a classifier which distinguishes between normal and anomalous behaviour patterns can be incrementally trained with the assistance of a human operator. We consider the behaviour of pedestrians in terms of motion trajectories, and parametrise these trajectories using the control points of approximating cubic spline curves. This paper demonstrates an incremental semi-supervised one-class learning procedure in which unlabelled trajectories are combined with occasional examples of normal behaviour labelled by a human operator. This procedure is found to be effective on two different datasets, indicating that a human operator could potentially train the system to detect anomalous behaviour by providing only occasional interventions (a small percentage of the total number of observations).
2008 by The University of Edinburgh. All Rights Reserved
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Bibtex format
author = { Rowland Sillito and Robert Fisher },
title = {Semi-supervised Learning for Anomalous Trajectory Detection},
book title = {Proc. British Machine Vision Conference (BMVC)},
year = 2008,
month = {Sep},
pages = {1035-1044},

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