STATIC FOREGROUND ANALYSIS TO DETECT ABANDONED OR REMOVED OBJECTS

Andrea Caroppo, Tommaso Martiriggiano, Marco Leo, Paolo Spagnolo, Tiziana D’Orazio

Abstract

In this paper, a new method to robustly and efficiently analyse video sequences to both extract foreground objects and to classify the static foreground regions as abandoned or removed objects (ghosts) is presented. As a first step, the moving regions in the scene are detected by subtracting to the current frame a referring model continuously adapted. Then, a shadow removing algorithm is used to find out the real shape of the detected objects and an homographic transformations is used to localize them in the scene avoiding perspective distortions. Finally, moving objects are classified as abandoned or removed by analysing the boundaries of static foreground regions. The method was successfully tested on real image sequences and it run about 7 fps at size 480x640 on a 2,33 GB Pentium IV machine.

References

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Paper Citation


in Harvard Style

Caroppo A., Martiriggiano T., Leo M., Spagnolo P. and D’Orazio T. (2006). STATIC FOREGROUND ANALYSIS TO DETECT ABANDONED OR REMOVED OBJECTS . In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 972-8865-40-6, pages 451-456. DOI: 10.5220/0001373104510456


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - STATIC FOREGROUND ANALYSIS TO DETECT ABANDONED OR REMOVED OBJECTS
SN - 972-8865-40-6
AU - Caroppo A.
AU - Martiriggiano T.
AU - Leo M.
AU - Spagnolo P.
AU - D’Orazio T.
PY - 2006
SP - 451
EP - 456
DO - 10.5220/0001373104510456


in Bibtex Style

@conference{visapp06,
author={Andrea Caroppo and Tommaso Martiriggiano and Marco Leo and Paolo Spagnolo and Tiziana D’Orazio},
title={STATIC FOREGROUND ANALYSIS TO DETECT ABANDONED OR REMOVED OBJECTS},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2006},
pages={451-456},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001373104510456},
isbn={972-8865-40-6},
}