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Computing a rodent’s diary

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Abstract

Rodent monitoring in biomedical laboratories is a time consuming and tedious task. Several automatic solutions that rely on different types of sensors have been proposed. Computer vision provides a significantly more universal and less intrusive solution. In this article we propose a new method to detect and classify three behaviors in rodents: exploring, rearing, and static. The method uses motion history images and a multiple classifier system to detect the three behaviors under typical laboratory conditions. It is independent of the color of the rodent and of the background. The method performs equally well on short and long video sequences, achieving a success rate of 87 %.

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Authors and Affiliations

  1. Computer and Software Engineering Department, Ecole Polytechnique de Montréal, 2900, boul. Édouard-Montpetit. Campus de l’Université de Montréal 2500, Chemin de Polytechnique, Montreal, Québec, H3T 1J4, Canada

    Rana Farah, J. M. Pierre Langlois & Guillaume-Alexandre Bilodeau

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  1. Rana Farah
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  2. J. M. Pierre Langlois
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  3. Guillaume-Alexandre Bilodeau
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Correspondence to Rana Farah.

Additional information

This work is funded by CRSNG (Conseil de recherche en science naturelles et en génie du Canada) and the FQRNT (The fonds de recherche du Québec—Nature et technologies).

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Farah, R., Langlois, J.M.P. & Bilodeau, GA. Computing a rodent’s diary. SIViP 10, 567–574 (2016). https://doi.org/10.1007/s11760-015-0776-2

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  • DOI: https://doi.org/10.1007/s11760-015-0776-2

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