基于深度学习的人体行为识别综述

人体行为识别旨在对视频监控中的人体行为进行检索并识别，是人工智能领域的研究热点。基于传统方法的人体行为识别算法存在对样本数据依赖大、易受环境噪声影响等不足。为解决此问题，许多适用于不同应用场景的基于深度学习的人体行为识别算法被提出。介绍了人体行为识别任务中传统特征提取方法和基于深度学习的特征提取方法；从性能和应用两方面对基于深度学习的人体行为识别算法进行总结，重点分析了基于3D卷积神经网络、混合网络、双流卷积神经网络和少样本学习（few-shot learning，FSL）的人体行为识别方法及其在UCF101和HMDB51数据集上的表现；在深度学习的基础上，归纳了主流模型迁移方法的优缺点及其有效性；总结了现有基于深度学习的人体行为识别算法存在的不足，并讨论了以元学习（meta-learning）和transformer为代表的FSL算法将成为未来模型主流算法的可能性，同时对未来基于深度学习的人体行为识别算法的发展方向进行展望。

Overview of Human Behavior Recognition Based on Deep Learning

Human behavior recognition aims to retrieve and identify the target behavior in surveillance video, which is a research hotspot in the field of artificial intelligence. Human behavior recognition algorithm based on traditional methods has some shortcomings, such as large dependence on sample data and easy to be affected by environmental noise. In order to solve this problem, many human behavior recognition algorithms based on deep learning are proposed for different application scenarios. Firstly, the traditional feature extraction methods and feature extraction methods based on deep learning in human behavior recognition task are introduced. Secondly, the human behavior recognition algorithms based on deep learning are summarized from two aspects of performance and application. The idea of human behavior recognition method based on 3D convolutional neural network, hybrid network, two-stream convolutional neural network and few-shot learning（FSL） and its performance on UCF101 and HMDB51 datasets are analyzed. Thirdly, on the basis of deep learning, the advantages, disadvantages and effectiveness of mainstream model migration methods are summarized. Finally, the shortcomings of existing human behavior recognition algorithms based on deep learning are summarized, and the possibility of FSL algorithm represented by meta-learning and transformer that will become the mainstream algorithm of future models is discussed. At the same time, the future development direction of human behavior recognition based on deep learning is prospected.