Research Progress in Application of Deep Learning in Animal Behavior Analysis
In recent years, animal behavior analysis has become one of the most important methods in the fields of neuroscience and artificial intelligence. Taking advantage of the powerful deep-learning-based image analysis technology, researchers have developed state-of-the-art automatic animal behavior anal...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2024-03-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2306033.pdf |
| _version_ | 1797271501569785856 |
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| author | SHEN Tong, WANG Shuo, LI Meng, QIN Lunming |
| author_facet | SHEN Tong, WANG Shuo, LI Meng, QIN Lunming |
| author_sort | SHEN Tong, WANG Shuo, LI Meng, QIN Lunming |
| collection | DOAJ |
| description | In recent years, animal behavior analysis has become one of the most important methods in the fields of neuroscience and artificial intelligence. Taking advantage of the powerful deep-learning-based image analysis technology, researchers have developed state-of-the-art automatic animal behavior analysis methods with complex functions. Compared with traditional methods of animal behavior analysis, special labeling is not required in these methods, animal pose can be efficiently estimated and tracked. These methods like in a natural environment, which hold the potential for complex animal behavior experiments. Therefore, the application of deep learning in animal behavior analysis is reviewed. Firstly, this paper analyzes the tasks and current status of animal behavior analysis. Then, it highlights and compares existing deep learning-based animal behavior analysis tools. According to the dimension of experimental analysis, the deep learning-based animal behavior analysis tools are divided into two-dimensional animal behavior analysis tools and three-dimensional animal behavior analysis tools, and the functions, performance and scope of application of tools are discussed. Furthermore, the existing animal datasets and evaluation metrics are introduced, and the algorithm mechanism used in the existing animal behavior analysis tool is summarized from the advantages, limitations and applicable scenarios. Finally, the deep learning-based animal behavior analysis tools are prospected from the aspects of dataset, experimental paradigm and low latency. |
| first_indexed | 2024-03-07T14:04:32Z |
| format | Article |
| id | doaj.art-fd0732e35fab40179fffc28cef722221 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-03-07T14:04:32Z |
| publishDate | 2024-03-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-fd0732e35fab40179fffc28cef7222212024-03-07T02:27:38ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182024-03-0118361262610.3778/j.issn.1673-9418.2306033Research Progress in Application of Deep Learning in Animal Behavior AnalysisSHEN Tong, WANG Shuo, LI Meng, QIN Lunming01. College of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China 2. Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, ChinaIn recent years, animal behavior analysis has become one of the most important methods in the fields of neuroscience and artificial intelligence. Taking advantage of the powerful deep-learning-based image analysis technology, researchers have developed state-of-the-art automatic animal behavior analysis methods with complex functions. Compared with traditional methods of animal behavior analysis, special labeling is not required in these methods, animal pose can be efficiently estimated and tracked. These methods like in a natural environment, which hold the potential for complex animal behavior experiments. Therefore, the application of deep learning in animal behavior analysis is reviewed. Firstly, this paper analyzes the tasks and current status of animal behavior analysis. Then, it highlights and compares existing deep learning-based animal behavior analysis tools. According to the dimension of experimental analysis, the deep learning-based animal behavior analysis tools are divided into two-dimensional animal behavior analysis tools and three-dimensional animal behavior analysis tools, and the functions, performance and scope of application of tools are discussed. Furthermore, the existing animal datasets and evaluation metrics are introduced, and the algorithm mechanism used in the existing animal behavior analysis tool is summarized from the advantages, limitations and applicable scenarios. Finally, the deep learning-based animal behavior analysis tools are prospected from the aspects of dataset, experimental paradigm and low latency.http://fcst.ceaj.org/fileup/1673-9418/PDF/2306033.pdfanimal behavior analysis methods; deep learning; animal pose estimation |
| spellingShingle | SHEN Tong, WANG Shuo, LI Meng, QIN Lunming Research Progress in Application of Deep Learning in Animal Behavior Analysis Jisuanji kexue yu tansuo animal behavior analysis methods; deep learning; animal pose estimation |
| title | Research Progress in Application of Deep Learning in Animal Behavior Analysis |
| title_full | Research Progress in Application of Deep Learning in Animal Behavior Analysis |
| title_fullStr | Research Progress in Application of Deep Learning in Animal Behavior Analysis |
| title_full_unstemmed | Research Progress in Application of Deep Learning in Animal Behavior Analysis |
| title_short | Research Progress in Application of Deep Learning in Animal Behavior Analysis |
| title_sort | research progress in application of deep learning in animal behavior analysis |
| topic | animal behavior analysis methods; deep learning; animal pose estimation |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2306033.pdf |
| work_keys_str_mv | AT shentongwangshuolimengqinlunming researchprogressinapplicationofdeeplearninginanimalbehavioranalysis |