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Automated quantification of the schooling behaviour of sticklebacks

Reza Ardekani1*, Anna K Greenwood2, Catherine L Peichel2 and Simon Tavaré13

Author Affiliations

1 Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA

2 Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA

3 DAMTP, University of Cambridge, Cambridge CB3 0WA, UK

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EURASIP Journal on Image and Video Processing 2013, 2013:61  doi:10.1186/1687-5281-2013-61

Published: 9 November 2013


Sticklebacks have long been used as model organisms in behavioural biology. An important anti-predator behaviour in sticklebacks is schooling. We plan to use quantitative trait locus mapping to identify the genetic basis for differences in schooling behaviour between marine and benthic sticklebacks. To do this, we need to quantify the schooling behaviour of thousands of fish. We have developed a robust high-throughput video analysis method that allows us to screen a few thousand individuals automatically. We propose a non-local background modelling approach that allows us to detect and track sticklebacks and obtain the schooling parameters efficiently.