Human activity recognition from 3D data: A review

被引：381

作者：

Aggarwal, J. K. ^{[1
]}

Xia, Lu ^{[1
]}

机构：

[1] Univ Texas Austin, Austin, TX 78705 USA

来源：

PATTERN RECOGNITION LETTERS | 2014年 / 48卷

关键词：

Computer vision; Human activity recognition; 3D data; Depth image; RANGE IMAGES; OPTICAL-FLOW; STEREO; MOTION; TRACKING; SHAPE; VISION; DENSE; HISTOGRAMS; INVARIANT;

D O I：

10.1016/j.patrec.2014.04.011

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Human activity recognition has been an important area of computer vision research since the 1980s. Various approaches have been proposed with a great portion of them addressing this issue via conventional cameras. The past decade has witnessed a rapid development of 3D data acquisition techniques. This paper summarizes the major techniques in human activity recognition from 3D data with a focus on techniques that use depth data. Broad categories of algorithms are identified based upon the use of different features. The pros and cons of the algorithms in each category are analyzed and the possible direction of future research is indicated. (C) 2014 Elsevier B. V. All rights reserved.

引用

收藏

页码：70 / 80

页数：11

相关论文

共 50 条

[31] HPOF: 3D Human Pose Recovery from Monocular Video with Optical Flow [J].

Ji, Bin ;

Yang, Chen ;

Yao, Shunyu ;

Pan, Ye .

PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL (ICMR '21), 2021, :144-154

[32] Human Activity Recognition: A review [J].

Pereira, Joao Goncalo ;

Goncalves, Joaquim .

2022 10TH INTERNATIONAL SYMPOSIUM ON DIGITAL FORENSICS AND SECURITY (ISDFS), 2022,

[33] Human Activity Recognition: A Review [J].

Ann, Ong Chin ;

Theng, Lau Bee .

2014 IEEE INTERNATIONAL CONFERENCE ON CONTROL SYSTEM COMPUTING AND ENGINEERING, 2014, :389-393

[34] Discriminative parts learning for 3D human action recognition [J].

Huang, Min ;

Cai, Guo-Rong ;

Zhang, Hong-Bo ;

Yu, Sheng ;

Gong, Dong-Ying ;

Cao, Dong-Lin ;

Li, Shaozi ;

Su, Song-Zhi .

NEUROCOMPUTING, 2018, 291 :84-96

[35] From Methods to Applications: A Review of Deep 3D Human Motion Capture [J].

Niu, Zehai ;

Lu, Ke ;

Xue, Jian ;

Qin, Xiaoyu ;

Wang, Jinbao ;

Shao, Ling .

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2024, 34 (11) :11340-11359

[36] Sequential Learning for Multimodal 3D Human Activity Recognition with Long-Short Term Memory [J].

Li, Kang ;

Zhao, Xiaoguang ;

Bian, Jiang ;

Tan, Min .

2017 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA), 2017, :1556-1561

[37] Multi-stream 3D CNN structure for human action recognition trained by limited data [J].

Chenarlogh, Vahid Ashkani ;

Razzazi, Farbod .

IET COMPUTER VISION, 2019, 13 (03) :338-344

[38] Contact Part Detection From 3D Human Motion Data Using Manually Labeled Contact Data and Deep Learning [J].

Kang, Changgu ;

Kim, Meejin ;

Kim, Kangsoo ;

Lee, Sukwon .

IEEE ACCESS, 2023, 11 :127608-127618

[39] Survey on Human Activity Recognition based on Acceleration Data [J].

Slim, Salwa O. ;

Atia, Ayman ;

Elfattah, Marwa M. A. ;

Mostafa, Mostafa-Sami M. .

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2019, 10 (03) :84-98

[40] 3D Face Tracking Using Stereo Cameras: A Review [J].

Alqahtani, Faleh ;

Banks, Jasmine ;

Chandran, Vinod ;

Zhang, Jinglan .

IEEE ACCESS, 2020, 8 :94373-94393

← 1 2 3 4 5 →