Nguyen, Ngoc G and Phan, Dau and Lumbanraja, Favorisen R and Faisal, Mohammad Reza and Abapihi, Bahriddin and Purnama, Bedy and Delimayanti, Mera K and Mahmudah, Kunti R and Kubo, Mamoru and Satou, Kenji (2019) Applying Deep Learning Models to Mouse Behavior Recognition. Journal of Biomedical Science and Engineering, 12 (2). pp. 183-196. ISSN 937-6871
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Abstract
n 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.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Ilmu Komputer |
| Depositing User: | Favorisen R Lumbanraja |
| Date Deposited: | 05 Apr 2019 08:03 |
| Last Modified: | 05 Apr 2019 08:03 |
| URI: | http://repository.lppm.unila.ac.id/id/eprint/10166 |
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