Author(s)

Ngoc Giang Nguyen¹, Dau Phan¹, Favorisen Rosyking Lumbanraja¹, Mohammad Reza Faisal¹, Bahriddin Abapihi¹, Bedy Purnama¹, Mera Kartika Delimayanti¹, Kunti Robiatul Mahmudah¹, Mamoru Kubo², Kenji Satou²

¹Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan.
²Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan.

In many animal-related studies, a high-performance animal behavior recognition system can help researchers reduce or get rid of the limitation of human assessments and make the experiments easier to reproduce. Recently, although deep learning models are holding state-of-the-art performances in human action recognition tasks, these models are not well-studied in applying to animal behavior recognition tasks. One reason is the lack of extensive datasets which are required to train these deep models for good performances. In this research, we investigated two current state-of-the-art deep learning models in human action recognition tasks, the I3D model and the R(2 + 1)D model, in solving a mouse behavior recognition task. We compared their performances with other models from previous researches and the results showed that the deep learning models that pre-trained using human action datasets then fine-tuned using the mouse behavior dataset can outperform other models from previous researches. It also shows promises of applying these deep learning models to other animal behavior recognition tasks without any significant modification in the models’ architecture, all we need to do is collecting proper datasets for the tasks and fine-tuning the pre-trained models using the collected data.

Mouse Behavior Recognition, Deep Learning, I3D Models, R(2 + 1)D Models

Nguyen, N. , Phan, D. , Lumbanraja, F. , Faisal, M. , Abapihi, B. , Purnama, B. , Delimayanti, M. , Mahmudah, K. , Kubo, M. and Satou, K. (2019) Applying Deep Learning Models to Mouse Behavior Recognition. Journal of Biomedical Science and Engineering, 12, 183-196. doi: 10.4236/jbise.2019.122012.