• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2021, Vol. 43 ›› Issue (12): 2206-2215.

• 图形与图像 • 上一篇    下一篇

基于深度学习的人体行为检测方法研究综述

陆卫忠1,2,宋正伟1,吴宏杰1,2,曹燕1,丁漪杰1,2 ,张郁3


  

  1. (1.苏州科技大学电子与信息工程学院,江苏 苏州 215009; 

    2.江苏省建筑智慧节能重点实验室,江苏 苏州 215009; 3.苏州工业园区工业技术学校,江苏 苏州 215123)

  • 收稿日期:2020-04-30 修回日期:2020-09-08 接受日期:2021-12-25 出版日期:2021-12-25 发布日期:2021-12-31
  • 基金资助:
    国家自然科学基金(61472267,61902271,61772357,61902272,61672371,61876217,61750110519);苏州市科技项目(SYG201704,SNG201610,SZS201609)

Overview of human behavior detection methods based on deep learning

LU Wei-zhong1,2,SONG Zheng-wei1,WU Hong-jie1,2,CAO Yan1,DING Yi-jie1,2,ZHANG Yu3   

  1. (1.School of Electronic and Information Engineering,Suzhou University of Science and Technology,Suzhou 215009;

    2.Jiangsu Provincial Key Laboratory of Building Intelligence and Energy Saving,Suzhou 215009;

    3.Suzhou Industrial Park Industrial Technology School,Suzhou 215123,China)
  • Received:2020-04-30 Revised:2020-09-08 Accepted:2021-12-25 Online:2021-12-25 Published:2021-12-31

摘要: 行为检测是视频理解与计算机视觉领域炙手可热的研究内容,备受国内外学者的关注,在智能监控、人机交互等多领域被广泛应用。随着科技的进步,深度学习在图像分类领域取得了重大突破,将基于深度学习的识别方法应用于人体行为检测研究已成为行为检测中的热点。基于此,首先对几种常用于行为检测的数据集,及近几年深度学习在行为检测领域的研究现状进行了介绍;接着分析了行为检测方法的基本流程,以及几种常用的基于深度学习的检测方法;最后,从方法性能优劣、应用前景等方面对人体行为检测方法的尚存问题与未来发展趋势进行了分析和展望。


关键词: 深度学习, 人体行为检测, 智能监控, 行为数据集

Abstract: Behavior detection is a research hotspot in the field of video understanding and computer vision, which attracts the attention of scholars at home and abroad. It has been widely used in many fields such as intelligent surveillance and human-computer interaction. With the development of techno- logy, deep learning has made a great breakthrough in image classification. The application of the recognition methods based on deep learning to human behavior detection has become a hotspot. Therefore, the paper firstly introduces several datasets commonly used in behavior detection, and the research status of deep learning in the field of behavior detection in recent years. Then, the basic process of behavior detection methods and recognition methods based on deep learning are analyzed. Finally, the future development trend and possible shortcomings are analyzed from the aspects of method performance and application prospect.


Key words: deep learning, human behavior detection, intelligent surveillance, behavior dataset