Unsupervised Discovery of Normal and Abnormal Activity Patterns in Indoor and Outdoor Environments

Dario Dotti, Mirela Popa, Stylianos Asteriadis

2017

Abstract

In this paper we propose an adaptive system for monitoring indoor and outdoor environments using movement patterns. Our system is able to discover normal and abnormal activity patterns in absence of any prior knowledge. We employ several feature descriptors, by extracting both spatial and temporal cues from trajectories over a spatial grid. Moreover, we improve the initial feature vectors by applying sparse autoencoders, which help at obtaining optimized and compact representations and improved accuracy. Next, activity models are learnt in an unsupervised manner using clustering techniques. The experiments are performed on both indoor and outdoor datasets. The obtained results prove the suitability of the proposed system, achieving an accuracy of over 98% in classifying normal vs. abnormal activity patterns for both scenarios. Furthermore, a semantic interpretation of the most important regions of the scene is obtained without the need of human labels, highlighting the flexibility of our method.

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Paper Citation


in Harvard Style

Dotti D., Popa M. and Asteriadis S. (2017). Unsupervised Discovery of Normal and Abnormal Activity Patterns in Indoor and Outdoor Environments . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 210-217. DOI: 10.5220/0006116902100217


in Bibtex Style

@conference{visapp17,
author={Dario Dotti and Mirela Popa and Stylianos Asteriadis},
title={Unsupervised Discovery of Normal and Abnormal Activity Patterns in Indoor and Outdoor Environments},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={210-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006116902100217},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Unsupervised Discovery of Normal and Abnormal Activity Patterns in Indoor and Outdoor Environments
SN - 978-989-758-226-4
AU - Dotti D.
AU - Popa M.
AU - Asteriadis S.
PY - 2017
SP - 210
EP - 217
DO - 10.5220/0006116902100217