A deep learning-based approach for real-time rodent detection and behaviour classification

Animal models are helpful to evaluate the effects of some drugs in the treatment of brain diseases, such as the case of the Open Field Maze. Usually, these tests are recorded in video and analysed afterwards to carry out manual annotations about the activity and behaviour of the rat. Usually, these videos must be watched repeatedly to ensure correct annotations, but they are prone to become a tedious task and are highly likely to produce human errors. Existing commercial systems for automatic detection of the rat and classification of its behaviours may become inaccessible for research teams that cannot afford the license cost. Motivated by the latter, we propose a methodology for simultaneous rat detection and behaviour classification using inexpensive hardware in this work. Our proposal is a Deep Learning-based two-step methodology to simultaneously detect the rat in the test and classify its behaviour. In the first step, a single shot detector network is used to detect the rat; then, the systems crop the image using the bounding box to generate a sequence of six images that input our BehavioursNet network to classify the rodent's behaviour. Finally, based on the results of these steps, the system generates an ethogram for the complete video, a trajectory plot, a heatmap plot for most visited regions and a video showing the rat's detection and its behaviours. Our results show that it is possible to perform these tasks at a processing rate of 23 Hz, with a low error of 6 pixels in the detection and a first approach to classify ambiguous behaviours such as resting and grooming, with an average precision of 60%, which is competitive with that reported in the literature.