Computing a rodent's diary. Issue 3 (March 2016) - Farah, Rana; Langlois, J.; Bilodeau, Guillaume-Alexandre
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 %.