ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (1): 93-112.doi: 10.7544/issn1000-1239.2016.20150403

所属专题: 2016优青专题

• 其他应用技术 • 上一篇    下一篇

人的视觉行为识别研究回顾、现状及展望

单言虎,张彰,黄凯奇   

  1. (模式识别国家重点实验室(中国科学院自动化研究所) 北京 100080) (yanhu.shan@nlpr.ia.ac.cn)
  • 出版日期: 2016-01-01
  • 基金资助: 
    国家自然科学基金项目(61322209,61473290);国家“九七三”重点基础研究发展计划基金项目(2012CB316302);新疆维吾尔族自治区科技专项基金项目(201230122)

Visual Human Action Recognition: History, Status and Prospects

Shan Yanhu, Zhang Zhang, Huang Kaiqi   

  1. (National Laboratory of Pattern Recognition (Institute of Automation, Chinese Academy of Sciences), Beijing 100080)
  • Online: 2016-01-01

摘要: 人的行为识别是计算机视觉领域中的重点研究问题之一.相对于静态图像中物体识别研究,行为识别更加关注如何感知感兴趣目标在图像序列中的时空运动变化.视觉行为的存在方式从二维空间到三维时空的扩展大大增加了行为表达及后续识别任务的复杂性,同时也为视觉研究者提供了更广阔的空间以尝试不同的解决思路和技术方法.近年来,人的行为识别相关工作层出不穷,已成为计算机视觉研究中的热点方向.以时间为顺序,对从21世纪初至今约15年中出现的视觉行为识别研究方法进行了梳理、归类和总结.相比其他综述性文章,以不同时期人的行为识别数据库的演化为线索,介绍不同时期行为识别研究所关注的研究重点问题和主要研究思路,能更清晰直观地体现行为识别研究的发展历程.同时,以数据库演化历程为顺序介绍行为识别,能更好地呼应当前视觉领域愈来愈受人关注的大数据驱动的研究思路.通过对相关工作的梳理和总结,还对今后行为识别研究的发展方向做出展望,希望对各位研究者方向把握上提供一些帮助.

关键词: 计算机视觉, 行为识别, 时空运动, 数据库演化, 综述

Abstract: Human action recognition is an important issue in the field of computer vision. Compared with object recognition in still images, human action recognition has more concerns on the spatio-temporal motion changes of interesting objects in image sequences. The extension of 2D image to 3D spatio-temporal image sequence increases the complexity of action recognition greatly, Meanwhile, it also provides a wide space for various attempts on different solutions and techniques on human action recognition. Recently, many new algorithms and systems on human action recognition have emerged, which indicates that it has become one of the hottest topics in computer vision. In this paper, we propose a taxonomy of human action recognition in chronological order to classify action recognition methods into different periods and put forward general summaries of them. Compared with other surveys, the proposed taxonomy introduces human action recognition methods and summarizes their characteristics by analyzing the action dataset evolution and responding recognition methods. Furthermore, the introduction of action recognition datasets coincides with the trend of big data-driven research idea. Through the summarization on related work, we also give some prospects on future work.

Key words: computer vision, action recognition, spatio-temporal motion, dataset evolution, survey

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