Authors:
Dario Dotti
;
Mirela Popa
and
Stylianos Asteriadis
Affiliation:
Maastricht University, Netherlands
Keyword(s):
Ambient Assisted Living, Video Surveillance, Unsupervised Learning, Movement Histograms, Scene Understanding.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
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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