Publication
Title
Automated social behaviour recognition at low resolution
Author
Abstract
Automated behaviour recognition is a challenging problem and it has recently gained momentum in biological behaviour studies. This paper describes a framework for tracking and automatical classification of the behaviour of multiple freely interacting Drosophila Melanogaster (fruit flies) in a low resolution video. The movements of interacting flies are recorded by Fly world, a dedicated imaging platform. Each individual fly is identified in every frame and tracked over the complete video without losing its identity. The orientation of the flies is tracked as well, by defining their head and tail positions. From the obtained tracks, temporal features for every pair of fly are derived, allowing quantitative analysis of the fly behaviour. In order to derive information of the fly social activity, we concentrate on 2 specific behaviours: 'sniffing' and 'chasing'. Experimental results show that the classifier is able to classify the correct behaviour with an average overall accuracy of 95.46%.
Language
English
Source (journal)
Proceedings of the IAPR international conference on pattern recognition / IAPR International Conference on Pattern Recognition. - Los Alamitos
Source (book)
ICPR14 : International Conference on Pattern Recognition, Stockholm, Sweden, August 24-28, 2014
Publication
Los Alamitos, Calif. : IEEE, 2014
ISBN
978-1-4799-5208-3
Volume/pages
(2014), p. 2323-2328
ISI
000359818002074
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 16.02.2015
Last edited 13.08.2017
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