Abnormal Event Detection using Scene Partitioning by Regional Activity Pattern Analysis

Jongmin Yu, Jeonghwan Gwak, Seongjong Noh, Moongu Jeon

2016

Abstract

This paper presents a method for detecting abnormal events based on scene partitioning. To develop the practical application for abnormal event detection, the proposed method focuses on handling various activity patterns caused by diverse moving objects and geometric conditions such as camera angles and distances between the camera and objects. We divide a frame into several blocks and group the blocks with similar motion patterns. Then, the proposed method constructs normal-activity models for local regions by using the grouped blocks. These regional models allow to detect unusual activities in complex surveillance scenes by considering specific regional local activity patterns. We construct a new dataset called GIST Youtube dataset, using the Youtube videos to evaluate performance in practical scenes. In the experiments, we used the dataset of the university of minnesota, and our dataset. From the experimental study, we verified that the proposed method is efficient in the complex scenes which contain the various activity patterns.

Paper Citation

in Harvard Style

Yu J., Gwak J., Noh S. and Jeon M. (2016). Abnormal Event Detection using Scene Partitioning by Regional Activity Pattern Analysis . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 634-641. DOI: 10.5220/0005720606340641

in Bibtex Style

@conference{visapp16,
author={Jongmin Yu and Jeonghwan Gwak and Seongjong Noh and Moongu Jeon},
title={Abnormal Event Detection using Scene Partitioning by Regional Activity Pattern Analysis},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={634-641},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005720606340641},
isbn={978-989-758-175-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Abnormal Event Detection using Scene Partitioning by Regional Activity Pattern Analysis
SN - 978-989-758-175-5
AU - Yu J.
AU - Gwak J.
AU - Noh S.
AU - Jeon M.
PY - 2016
SP - 634
EP - 641
DO - 10.5220/0005720606340641