Automated social behaviour recognition at low resolution
Faculty of Sciences. Physics
Los Alamitos, Calif. :IEEE
Proceedings of the IAPR international conference on pattern recognition / IAPR International Conference on Pattern Recognition. - Los Alamitos
ICPR14 : International Conference on Pattern Recognition, Stockholm, Sweden, August 24-28, 2014
, p. 2323-2328
University of Antwerp
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%.