Mice Tracking Using The YOLO Algorithm

  • Richardson Santiago Teles de Menezes UFRN
  • John Victor Alves Luiz UFRN
  • Aron Miranda Henrique-Alves UFRN
  • Rossana Moreno Santa Cruz IFPB
  • Helton Maia UFRN


The computational tool developed in this study is based on convolutional neural networks and the You Only Look Once (YOLO) algorithm for detecting and tracking mice in videos recorded during behavioral neuroscience experiments. 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. Considering the high accuracy of the results, the developed work allows the experimentalists to perform mice tracking in a reliable and non-evasive way.

Palavras-chave: convolutional neural networks, YOLO algorithm, neuroscience experiments


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DE MENEZES, Richardson Santiago Teles; LUIZ, John Victor Alves ; HENRIQUE-ALVES, Aron Miranda; SANTA CRUZ, Rossana Moreno; MAIA, Helton . Mice Tracking Using The YOLO Algorithm. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 47. , 2020, Cuiabá. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 162-173. ISSN 2595-6205. DOI: https://doi.org/10.5753/semish.2020.11326.