Applying Deep Learning Models to Mouse Behavior Recognition

作     者：Ngoc Giang Nguyen Dau Phan Favorisen Rosyking Lumbanraja Mohammad Reza Faisal Bahriddin Abapihi Bedy Purnama Mera Kartika Delimayanti Kunti Robiatul Mahmudah Mamoru Kubo Kenji Satou 

作者机构：Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan 

出 版 物：《Journal of Biomedical Science and Engineering》 (生物医学工程（英文）)

年 卷 期：2019年第12卷第2期

页      面：183-196页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Mouse Behavior Recognition Deep Learning I3D Models R(2 + 1)D Models 

摘      要：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.