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.

Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications

“…Nevertheless, it is deemed to be unsuitable if all benchmark algorithms are compared with this work, as: (1) the camera could be constantly moving and giving occasional severe motion blur, while most of the other proposed research were designed with a video stream with the FoV being fixed; and (2) the work focuses on a customizable surveillance UAV system, in which it is preferred to assess the system and its algorithm on an embedded computation unit (with a suitable real-time speed); however, many of the state-of-the-art methods require high computation power. Hence, we only compared our algorithm with Opromolla et al [ 48 ] and Peixoto et al [ 49 ], where they deployed similar tracking techniques based on the YOLO detector. The compared system was able to be executed on the designed hardware architecture in real time.…”

Object Recognition Using Convolutional Neural Networks

“…Our approach, as in [22], also used two versions of the YOLO network to detect mice within three different experimental setups. The results obtained were based on the analysis of 13,622 images, organized according to the dataset described in Section 3.…”

“…Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems [26]. Additionally, results from the YOLO network, reproduced from [22], to detect and track mice in videos are recorded during Table 2.…”

“…To further illustrate the applications of such algorithms in scientific research, the dataset used for the YOLO network presented in [22] was also analyzed. As described in [22], the dataset is composed of images from three researches that involve behavioral experiments with mice: Faster RCNN acts as a single, unified network for object detection [15]. The region proposal network module serves as the "attention" of this unified network.…”

“…The same dataset division used in [22] was also reproduced resulting in 6811 images for training, 3405 for validation, and 3406 for the test.…”