Real-time Multiple Abnormality Detection in Video Data

Simon Hartmann Have, Huamin Ren, Thomas B. Moeslund


Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets show that the proposed framework can reliably detect abnormalities in the video sequence, outperforming the current state-of-the-art methods.


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Paper Citation

in Harvard Style

Have S., Ren H. and Moeslund T. (2013). Real-time Multiple Abnormality Detection in Video Data . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 390-395. DOI: 10.5220/0004280703900395

in Bibtex Style

author={Simon Hartmann Have and Huamin Ren and Thomas B. Moeslund},
title={Real-time Multiple Abnormality Detection in Video Data},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},

in EndNote Style

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - Real-time Multiple Abnormality Detection in Video Data
SN - 978-989-8565-48-8
AU - Have S.
AU - Ren H.
AU - Moeslund T.
PY - 2013
SP - 390
EP - 395
DO - 10.5220/0004280703900395