深度学习在动物行为分析中的应用研究进展
摘要： 近年来动物行为分析已成为脑科学与人工智能等领域的重要研究手段之一，研究者基于深度学习的图像分析技术，构建了自动化、智能化的动物行为分析方法。相较于传统的动物行为分析方法，该类方法无需对动物进行特殊标记，可高效地对动物的姿态进行估计和跟踪，实验情景贴近自然情况，为复杂的动物行为实验提供了潜在可能性。对深度学习在动物行为分析中的应用研究进行综述，首先简要分析动物行为分析的任务及现状；然后重点介绍并比较现有的基于深度学习的动物行为分析工具，根据实验分析的行为维度，将该类工具分为二维的动物行为分析工具和三维的动物行为分析工具，并对工具的功能、性能以及适用范围进行论述；进而介绍了现有的动物数据集和评价指标，对现有的动物行为分析工具所利用的算法机制从优势、局限性、适用场景做出总结；最后，从数据集、实验范式和低延时性等方面对基于深度学习的动物行为分析工具做出展望。
关键词: 动物行为分析方法, 深度学习, 动物姿态估计
Abstract: 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.
Key words: animal behavior analysis methods, deep learning, animal pose estimation
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