深度学习在动物行为分析中的应用研究进展OA北大核心CSTPCD

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 tech-nology,researchers have developed state-of-the-art automatic animal behavior analysis methods with complex func-tions.Compared with traditional methods of animal behavior analysis,special labeling is not required in these meth-ods,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 behav-ior 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 ani-mal 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.