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Authors: Bertrand Luvison 1 ; Thierry Chateau 1 ; Patrick Sayd 2 ; Quoc-Cuong Pham 2 and Jean-Thierry Lapresté 1

Affiliations: 1 Blaise Pascal University, France ; 2 Embedded Vision Systems Laboratory, France

Keyword(s): Motion learning, Kernel machine, Video surveillance.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Human-Computer Interaction ; Image and Video Analysis ; Methodologies and Methods ; Motion and Tracking ; Motion, Tracking and Stereo Vision ; Pattern Recognition ; Physiological Computing Systems ; Software Engineering ; Tracking of People and Surveillance ; Video Analysis

Abstract: This paper presents a generic unsupervised learning based solution to unexpected event detection from a static uncalibrated camera. The system can be represented into a probabilistic framework in which the detection is achieved by a likelihood based decision. We propose an original method to approximate the likelihood function using a sparse vector machine based model. This model is then used to detect efficiently unexpected events online. Moreover, features used are based on optical flow orientation within image blocks. The resulting application is able to learn automatically expected optical flow orientations from training video sequences and to detect unexpected orientations (corresponding to unexpected event) in a near real-time frame rate. Experiments show that the algorithm can be used in various applications like crowd or traffic event detection.

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Paper citation in several formats:
Luvison, B.; Chateau, T.; Sayd, P.; Pham, Q. and Lapresté, J. (2009). AN UNSUPERVISED LEARNING BASED APPROACH FOR UNEXPECTED EVENT DETECTION. In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications (VISIGRAPP 2009) - Volume 1: VISAPP; ISBN 978-989-8111-69-2; ISSN 2184-4321, SciTePress, pages 506-513. DOI: 10.5220/0001796705060513

@conference{visapp09,
author={Bertrand Luvison. and Thierry Chateau. and Patrick Sayd. and Quoc{-}Cuong Pham. and Jean{-}Thierry Lapresté.},
title={AN UNSUPERVISED LEARNING BASED APPROACH FOR UNEXPECTED EVENT DETECTION},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications (VISIGRAPP 2009) - Volume 1: VISAPP},
year={2009},
pages={506-513},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001796705060513},
isbn={978-989-8111-69-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications (VISIGRAPP 2009) - Volume 1: VISAPP
TI - AN UNSUPERVISED LEARNING BASED APPROACH FOR UNEXPECTED EVENT DETECTION
SN - 978-989-8111-69-2
IS - 2184-4321
AU - Luvison, B.
AU - Chateau, T.
AU - Sayd, P.
AU - Pham, Q.
AU - Lapresté, J.
PY - 2009
SP - 506
EP - 513
DO - 10.5220/0001796705060513
PB - SciTePress

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