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Automatically detecting and tracking free‐ranging Japanese macaques in video recordings with deep learning and particle filters
Author(s) -
Ueno Masataka,
Hayashi Hidetaka,
Kabata Ryosuke,
Terada Kazunori,
Yamada Kazunori
Publication year - 2019
Publication title -
ethology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.739
H-Index - 74
eISSN - 1439-0310
pISSN - 0179-1613
DOI - 10.1111/eth.12851
Subject(s) - artificial intelligence , ranging , computer science , computer vision , deep learning , particle filter , macaque , pattern recognition (psychology) , tracking (education) , classifier (uml) , track (disk drive) , filter (signal processing) , biology , psychology , telecommunications , paleontology , pedagogy , operating system
Recently, automated observation systems for animals using artificial intelligence have been proposed. In the wild, animals are difficult to detect and track automatically because of lamination and occlusions. Our study proposes a new approach to automatically detect and track wild Japanese macaques ( Macaca fuscata ) using deep learning and a particle filter algorithm. Macaque likelihood is derived through deep learning and used as an observation model in a particle filter to predict the macaques’ position and size in an image. By using deep learning as an observation model, it is possible to simplify the observation model and improve the accuracy of the classifier. We investigated whether the algorithm could find body regions of macaques in video recordings of free‐ranging groups at Katsuyama, Japan to evaluate our model. Experimental results showed that our method with deep learning as an observation model had higher tracking accuracy than a method that uses a support vector machine. More generally, our study will help researchers to develop automatic observation systems for animals in the wild.