Automatic fall detection of human in video using combination of features

Kun Wang; Guitao Cao; Dan Meng; Weiting Chen; Wenming Cao

doi:10.1109/BIBM.2016.7822694

Automatic fall detection of human in video using combination of features

Kun Wang
, Guitao Cao^*
, Dan Meng
, Weiting Chen
, Wenming Cao

^*Corresponding author for this work

Software Engineering Institute

East China Normal University
Shenzhen University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

47 Scopus citations

Abstract

The problem of automatically fall detection of older people living alone is a popular research topic since falls are one of the major health hazards among the aging population aged 65 and above and the population of them in China is more than 100 million. In this paper, we present an automatic human fall detection framework based on video surveillance which can improve safety of elders in indoor environments. First, a vision component was used to detect and extract moving people in videos from static cameras. Then, we combine Histograms of Oriented Gradients(HOG),Local Binary Pattern(LBP)and feature extracted by the Deep Learning Framework Caffe to form a new augmented feature and the feature is named HLC. We use HLC to represent a person's motion state in a frame of a video sequence. Because the process of fall is a sequence of movements, we use HLC features which were extracted from continuous frames of a video sequence to implement the fall detection. With the help of the HLC feature, we achieve an average fall detection result of 93.7% sensitivity and 92.0% specificity on three different datasets.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Editors	Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1228-1233
Number of pages	6
ISBN (Electronic)	9781509016105
DOIs	https://doi.org/10.1109/BIBM.2016.7822694
State	Published - 17 Jan 2017
Event	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China Duration: 15 Dec 2016 → 18 Dec 2016

Publication series

Name	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Conference

Conference	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/Territory	China
City	Shenzhen
Period	15/12/16 → 18/12/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Combination of features
Fall detection
Visual surveillance

Access to Document

10.1109/BIBM.2016.7822694

Cite this

Wang, K., Cao, G., Meng, D., Chen, W., & Cao, W. (2017). Automatic fall detection of human in video using combination of features. In K. Burrage, Q. Zhu, Y. Liu, T. Tian, Y. Wang, X. T. Hu, Q. Jiang, J. Song, S. Morishita, K. Burrage, & G. Wang (Eds.), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 1228-1233). Article 7822694 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822694

Wang, Kun ; Cao, Guitao ; Meng, Dan et al. / Automatic fall detection of human in video using combination of features. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. editor / Kevin Burrage ; Qian Zhu ; Yunlong Liu ; Tianhai Tian ; Yadong Wang ; Xiaohua Tony Hu ; Qinghua Jiang ; Jiangning Song ; Shinichi Morishita ; Kevin Burrage ; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1228-1233 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

@inproceedings{ef436c21052f4daa979ab704240863c1,

title = "Automatic fall detection of human in video using combination of features",

abstract = "The problem of automatically fall detection of older people living alone is a popular research topic since falls are one of the major health hazards among the aging population aged 65 and above and the population of them in China is more than 100 million. In this paper, we present an automatic human fall detection framework based on video surveillance which can improve safety of elders in indoor environments. First, a vision component was used to detect and extract moving people in videos from static cameras. Then, we combine Histograms of Oriented Gradients(HOG),Local Binary Pattern(LBP)and feature extracted by the Deep Learning Framework Caffe to form a new augmented feature and the feature is named HLC. We use HLC to represent a person's motion state in a frame of a video sequence. Because the process of fall is a sequence of movements, we use HLC features which were extracted from continuous frames of a video sequence to implement the fall detection. With the help of the HLC feature, we achieve an average fall detection result of 93.7\% sensitivity and 92.0\% specificity on three different datasets.",

keywords = "Combination of features, Fall detection, Visual surveillance",

author = "Kun Wang and Guitao Cao and Dan Meng and Weiting Chen and Wenming Cao",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/BIBM.2016.7822694",

language = "英语",

series = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1228--1233",

editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, \{Xiaohua Tony\} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",

booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

address = "美国",

}

Wang, K, Cao, G, Meng, D, Chen, W & Cao, W 2017, Automatic fall detection of human in video using combination of features. in K Burrage, Q Zhu, Y Liu, T Tian, Y Wang, XT Hu, Q Jiang, J Song, S Morishita, K Burrage & G Wang (eds), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822694, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1228-1233, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 15/12/16. https://doi.org/10.1109/BIBM.2016.7822694

Automatic fall detection of human in video using combination of features. / Wang, Kun; Cao, Guitao; Meng, Dan et al.
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. ed. / Kevin Burrage; Qian Zhu; Yunlong Liu; Tianhai Tian; Yadong Wang; Xiaohua Tony Hu; Qinghua Jiang; Jiangning Song; Shinichi Morishita; Kevin Burrage; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1228-1233 7822694 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic fall detection of human in video using combination of features

AU - Wang, Kun

AU - Cao, Guitao

AU - Meng, Dan

AU - Chen, Weiting

AU - Cao, Wenming

PY - 2017/1/17

Y1 - 2017/1/17

N2 - The problem of automatically fall detection of older people living alone is a popular research topic since falls are one of the major health hazards among the aging population aged 65 and above and the population of them in China is more than 100 million. In this paper, we present an automatic human fall detection framework based on video surveillance which can improve safety of elders in indoor environments. First, a vision component was used to detect and extract moving people in videos from static cameras. Then, we combine Histograms of Oriented Gradients(HOG),Local Binary Pattern(LBP)and feature extracted by the Deep Learning Framework Caffe to form a new augmented feature and the feature is named HLC. We use HLC to represent a person's motion state in a frame of a video sequence. Because the process of fall is a sequence of movements, we use HLC features which were extracted from continuous frames of a video sequence to implement the fall detection. With the help of the HLC feature, we achieve an average fall detection result of 93.7% sensitivity and 92.0% specificity on three different datasets.

AB - The problem of automatically fall detection of older people living alone is a popular research topic since falls are one of the major health hazards among the aging population aged 65 and above and the population of them in China is more than 100 million. In this paper, we present an automatic human fall detection framework based on video surveillance which can improve safety of elders in indoor environments. First, a vision component was used to detect and extract moving people in videos from static cameras. Then, we combine Histograms of Oriented Gradients(HOG),Local Binary Pattern(LBP)and feature extracted by the Deep Learning Framework Caffe to form a new augmented feature and the feature is named HLC. We use HLC to represent a person's motion state in a frame of a video sequence. Because the process of fall is a sequence of movements, we use HLC features which were extracted from continuous frames of a video sequence to implement the fall detection. With the help of the HLC feature, we achieve an average fall detection result of 93.7% sensitivity and 92.0% specificity on three different datasets.

KW - Combination of features

KW - Fall detection

KW - Visual surveillance

UR - https://www.scopus.com/pages/publications/85013306244

U2 - 10.1109/BIBM.2016.7822694

DO - 10.1109/BIBM.2016.7822694

M3 - 会议稿件

AN - SCOPUS:85013306244

T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

SP - 1228

EP - 1233

BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

A2 - Burrage, Kevin

A2 - Zhu, Qian

A2 - Liu, Yunlong

A2 - Tian, Tianhai

A2 - Wang, Yadong

A2 - Hu, Xiaohua Tony

A2 - Jiang, Qinghua

A2 - Song, Jiangning

A2 - Morishita, Shinichi

A2 - Burrage, Kevin

A2 - Wang, Guohua

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Y2 - 15 December 2016 through 18 December 2016

ER -

Wang K, Cao G, Meng D, Chen W, Cao W. Automatic fall detection of human in video using combination of features. In Burrage K, Zhu Q, Liu Y, Tian T, Wang Y, Hu XT, Jiang Q, Song J, Morishita S, Burrage K, Wang G, editors, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1228-1233. 7822694. (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). doi: 10.1109/BIBM.2016.7822694

Automatic fall detection of human in video using combination of features

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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