@inproceedings{86eea19231c849169730ec907d46466a,
title = "Anomaly detection in crowd scenes via online adaptive one-class support vector machines",
abstract = "We propose a novel, online adaptive one-class support vector machines algorithm for anomaly detection in crowd scenes. Integrating incremental and decremental one-class support vector machines with a sliding buffer offers an efficient and effective scheme, which not only updates the model in an online fashion with low computational cost, but also discards obsolete patterns. Our method provides a unified framework to detect both global and local anomalies. Extensive experiments have been carried out on two benchmark datasets and the comparison to the state-of-the-art methods validates the advantages of our approach.",
keywords = "anomaly detection, crowd scenes, online learning, support vector machines",
author = "Hanhe Lin and Deng, {Jeremiah D.} and Woodford, {Brendon J.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Image Processing, ICIP 2015 ; Conference date: 27-09-2015 Through 30-09-2015",
year = "2015",
month = dec,
day = "10",
doi = "10.1109/ICIP.2015.7351239",
language = "English",
isbn = "978-1-4799-8338-4",
series = "Proceedings - International Conference on Image Processing",
publisher = "IEEE",
pages = "2434--2438",
booktitle = "2015 IEEE International Conference on Image Processing (ICIP)",
}