| 基于姿态估计的动物行为识别研究进展 |
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| 引用本文： | 吴赛赛,吴建寨,程国栋,张楷,邢丽玮,韩书庆.基于姿态估计的动物行为识别研究进展[J].中国农业大学学报,2023,28(6):22-35. |
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| 作者姓名： | 吴赛赛 吴建寨 程国栋 张楷 邢丽玮 韩书庆 |
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| 作者单位： | 中国农业科学院 农业信息研究所/农业农村部区块链农业应用重点实验室, 北京 100081 |
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| 基金项目： | 国家自然科学基金项目(32102600)；中国农业科学院科技创新工程项目(CAAS-ASTIP-2016-AII)；中央级公益性科研院所基本科研业务费专项(JBYW-AII-2022-06/36) |
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| 摘 要： | 为分析姿态估计在动物行为识别和动物福利研究中的应用和发展潜力，本文以基于深度学习的姿态估计方法为突破口，从二维和三维空间的角度综述了动物姿态估计研究进展和方向，并介绍常见数据集与评价指标；然后整合基于姿态估计的动物行为识别相关研究成果，重点介绍关键点检测和行为分类的算法及其特征选取方式。姿态估计为动作识别和行为分析等研究提供了骨架信息和运动特征，成为动物行为识别和异常信息预警的非接触式监测方法。但由于受到训练数据集较少的限制，相对于人体姿态估计，动物姿态估计研究和发展速度相对较慢。因此，近年来利用跨域学习的方式来进一步提升其性能成为一种新兴手段。本综述为动物智能行为识别、动物福利研究以及智慧养殖等相关研究者扩展研究思路和研究方法。
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| 关 键 词： | 动物行为识别 健康监测 姿态估计 关键点检测 行为分类 |
| 收稿时间： | 2022/9/5 0:00:00 |
Research progress of animal behavior recognition based on pose estimation |
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| WU Saisai,WU Jianzhai,CHENG Guodong,ZHANG Kai,XING Liwei,HAN Shuqing.Research progress of animal behavior recognition based on pose estimation[J].Journal of China Agricultural University,2023,28(6):22-35. |
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| Authors: | WU Saisai WU Jianzhai CHENG Guodong ZHANG Kai XING Liwei HAN Shuqing |
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| Institution: | Agricultural Information Institute/Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Beijing 100081, China |
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| Abstract: | In order to analyze the application and development potential of pose estimation in animal behavior recognition and animal welfare research, this paper took the deep learning-based pose estimation method as a breakthrough, summarized the research progress and directions of animal pose estimation from the perspective of two-dimensional and three-dimensional space, and introduced the common data sets and evaluation indexes. Then integrated the research results related to animal behavior recognition based on pose estimation, focused on the algorithms of keypoint detection and behavior classification and their features. Pose estimation provides skeleton information and motion features for tasks such as action recognition and behavior analysis, and became a non-contact monitoring method for animal behavior recognition and abnormal information warning. However, due to the limitation of small training data set, the research and development of animal pose estimation was relatively slow compared to human pose estimation. Therefore, the used of cross-domain learning to further improve its performance has become an emerging tool in recent years. This review expands research ideas and research methods for researchers related to intelligent animal behavior recognition, animal welfare research, and smart farming. |
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| Keywords: | animal behavior recognition health monitoring pose estimation keypoints detection behavior classification |
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