Mouse face tracking using convolutional neural networks

2016
Akkaya, İbrahim Batuhan
Laboratory mice are frequently used in biomedical studies. Facial expressions of mice provide important data about various issues. For this reason real time tracking of mice provide output to both researcher and software that operate on face image directly. Since body and face of mice is the same color and mice moves fast, tracking of face of mice is a challenging task. In recent years, methods that use artificial neural networks provide effective solutions to problems such as classification, decision making and object recognition thanks to their ability to abstract training dataset. Especially, convolutional neural networks, which are inspired by visual cortex of animals, are very successful in computer vision tasks. In this study, a method based on deep learning which uses convolutional neural networks is proposed for real time tracking of face of mice. Convolutional neural networks are good at extracting hierarchical features from training dataset. High level features contains semantic features and low level features has high spatial resolution. Target information is extracted from combination of low and high leve1673516l features by convolutional layer to achieve robust and accurate tracker. Although proposed method is specialized in tracking face of mouse, it can be adapted any target by changing training dataset. 

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Citation Formats
İ. B. Akkaya, “Mouse face tracking using convolutional neural networks,” M.S. - Master of Science, Middle East Technical University, 2016.