基于骨架的人体异常行为识别与检测研究进展
Research progress on skeleton-based human abnormal behavior recognition and detection
朱红蕾 1卫鹏娟 1徐志刚1
作者信息
- 1. 兰州理工大学计算机与通信学院,兰州 730050
- 折叠
摘要
人体异常行为识别与检测技术已广泛应用于各种领域.由于视频中存在的物体遮挡、光照及视角变化、复杂背景等问题,使得利用轻量级人体骨架数据处理此类实时任务成为竞争性工具.多数研究从不同角度对此任务相关方法进行综述,但缺少针对人体骨架的整理工作.对此,立足于骨架数据,系统地综述了深度学习背景下的人体异常行为识别与检测方法.首先,按照应用场景中目标个数的不同,分类总结了典型的人体姿态估计算法;其次,依据特征提取网络的不同,将异常行为识别方法分为5类,分别围绕CNN、RNN、GCN、Transformer以及混合模型展开对比分析;然后,从数据与标签的映射学习角度,对3类异常行为检测方法进行讨论;最后,介绍了基准数据集及其上相关算法的表现,并探讨了此任务所面临的挑战及展望,以期为本领域未来的研究提供参考.
Abstract
The technology of human abnormal behavior recognition and detection has been widely applied in various fields.Due to the problems such as object occlusion,illumination and visual angle changes and complex background in video,lightweight human skeleton becomes a competitive tool for processing such real-time tasks.Most researches have reviewed the methods relevant to this task from different perspectives,but there is a lack of work on human skeleton.Based on skeleton data,this paper systematically reviews the methods of human abnormal behavior recognition and detection under the background of deep learning.Firstly,according to the different number of targets in the application scenario,human pose estimation algorithms are classified and summarized.Secondly,based on the different feature extraction networks,the abnormal behavior recognition methods are divided into five categories,which are compared and analyzed around the CNN,RNN,GCN,Transformer and hybrid models.Then,from the perspective of data and label mapping learning,three types of abnormal behavior detection methods are discussed.Finally,the baseline datasets and the performance of related algorithms are introduced,and the challenges and prospects facing this task are discussed in order to provide reference for future research in this field.
关键词
人体骨架/异常行为识别/异常行为检测/深度学习/姿态估计算法/注意力机制Key words
human skeleton/abnormal behavior recognition/abnormal behavior detection/deep learning/pose estimation algorithm/attention mechanism引用本文复制引用
出版年
2024
NETL
NSTL
万方数据