Mice tracking using the YOLO algorithm

Abstract

The development of computational tools is essential for the development of new technologies, including experimental designs needed for behavioral neuroscience research. The computational tool developed in this study is based on the convolutional neural networks and the You Only Look Once (YOLO) algorithm for detecting and tracking mice in videos recorded during behavioral neuroscience experiments. The task of mice detection consists of determining the location in the image where the animals are present, for each frame acquired. In this work, we propose mice tracking using the YOLO algorithm, running on an NVIDIA GeForce GTX 1060 GPU. We analyzed a set of data composed of 13622 images, made up of behavioral videos of three important researches in this area. The training set used 50% of the images, 25% for validation and 25% for the tests. The results show that the mean Average Precision (mAP) reached by the developed system was 90.79% and 90.75% for the Full and Tiny versions of YOLO, respectively. It has also been found that the use of the Tiny version is a good alternative for experimental designs that require real-time response. Considering the high accuracy of the results, the developed work allows the experimentalists to perform mice tracking in a reliable and non-evasive way, avoiding common system errors that require delimitations of regions of interest (ROI) or even evasive luminous identifiers such as LED for tracking the animals.