Thermal imaging based elderly fall detection

Somasundaram Vadivelu; Sudakshin Ganesan; O. V.Ramana Murthy; Abhinav Dhall

doi:10.1007/978-3-319-54526-4_40

Thermal imaging based elderly fall detection

Somasundaram Vadivelu, Sudakshin Ganesan, O. V.Ramana Murthy, Abhinav Dhall

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

27 Citations (Scopus)

Abstract

Elderly fall detection is very special case of human action recognition from videos and has very practical application in old age home and nursing centers. Fall detection in its simplest form is a binary classification of fall event or other daily routine activities. Hence, the current trend of sophisticated techniques being developed for human action recognition, particularly with scenarios of large number of classes may not be required in elderly fall detection. However, other design considerations such as simplicity (ready to be deployed), privacy issues (not revealing the identity) are to focused and are the major contributions of this paper. The Spatio-Temporal Interest Points (STIP) and Fisher vector framework for human action recognition is established as baseline in this work. A novel optical flow based technique is proposed that yields better performance than the baseline. Further, a very economical thermal imaging based input modality is proposed. Along with the thermal images not revealing the identity of the persons, thermal images also aid human detection from backgrounds – a useful solution in computing the optical flow of human movements. The proposed solution is also validated on the KUL Simulated Fall dataset showing its generalization capability.

Original language	English
Title of host publication	Computer Vision - ACCV 2016 Workshops
Subtitle of host publication	ACCV 2016 International Workshops, Revised Selected Papers
Editors	Chu-Song Chen, Kai-Kuang Ma, Jiwen Lu
Publisher	Springer Verlag
Pages	541-553
Number of pages	13
ISBN (Electronic)	9783319545264
ISBN (Print)	9783319545257
DOIs	https://doi.org/10.1007/978-3-319-54526-4_40
Publication status	Published - 2017
Externally published	Yes
Event	13th Asian Conference on Computer Vision - Taipei, Taiwan, Republic of China Duration: 20 Nov 2016 → 24 Nov 2016 Conference number: 13th

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10118 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Asian Conference on Computer Vision
Abbreviated title	ACCV 2016
Country/Territory	Taiwan, Republic of China
City	Taipei
Period	20/11/16 → 24/11/16

Keywords

Optical flow
Gaussian Mixture Model (GMM)
Interest point
Video segment
Fall detection

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10.1007/978-3-319-54526-4_40Licence: In Copyright

Cite this

Vadivelu, S., Ganesan, S., Murthy, O. V. R., & Dhall, A. (2017). Thermal imaging based elderly fall detection. In C.-S. Chen, K.-K. Ma, & J. Lu (Eds.), Computer Vision - ACCV 2016 Workshops: ACCV 2016 International Workshops, Revised Selected Papers (pp. 541-553). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10118 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-54526-4_40

Vadivelu, Somasundaram ; Ganesan, Sudakshin ; Murthy, O. V.Ramana et al. / Thermal imaging based elderly fall detection. Computer Vision - ACCV 2016 Workshops: ACCV 2016 International Workshops, Revised Selected Papers. editor / Chu-Song Chen ; Kai-Kuang Ma ; Jiwen Lu. Springer Verlag, 2017. pp. 541-553 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Elderly fall detection is very special case of human action recognition from videos and has very practical application in old age home and nursing centers. Fall detection in its simplest form is a binary classification of fall event or other daily routine activities. Hence, the current trend of sophisticated techniques being developed for human action recognition, particularly with scenarios of large number of classes may not be required in elderly fall detection. However, other design considerations such as simplicity (ready to be deployed), privacy issues (not revealing the identity) are to focused and are the major contributions of this paper. The Spatio-Temporal Interest Points (STIP) and Fisher vector framework for human action recognition is established as baseline in this work. A novel optical flow based technique is proposed that yields better performance than the baseline. Further, a very economical thermal imaging based input modality is proposed. Along with the thermal images not revealing the identity of the persons, thermal images also aid human detection from backgrounds – a useful solution in computing the optical flow of human movements. The proposed solution is also validated on the KUL Simulated Fall dataset showing its generalization capability.",

keywords = "Optical flow, Gaussian Mixture Model (GMM), Interest point, Video segment, Fall detection",

author = "Somasundaram Vadivelu and Sudakshin Ganesan and Murthy, {O. V.Ramana} and Abhinav Dhall",

year = "2017",

doi = "10.1007/978-3-319-54526-4_40",

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isbn = "9783319545257",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Chu-Song Chen and Kai-Kuang Ma and Jiwen Lu",

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Vadivelu, S, Ganesan, S, Murthy, OVR & Dhall, A 2017, Thermal imaging based elderly fall detection. in C-S Chen, K-K Ma & J Lu (eds), Computer Vision - ACCV 2016 Workshops: ACCV 2016 International Workshops, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10118 LNCS, Springer Verlag, pp. 541-553, 13th Asian Conference on Computer Vision, Taipei, Taiwan, Republic of China, 20/11/16. https://doi.org/10.1007/978-3-319-54526-4_40

Thermal imaging based elderly fall detection. / Vadivelu, Somasundaram; Ganesan, Sudakshin; Murthy, O. V.Ramana et al.
Computer Vision - ACCV 2016 Workshops: ACCV 2016 International Workshops, Revised Selected Papers. ed. / Chu-Song Chen; Kai-Kuang Ma; Jiwen Lu. Springer Verlag, 2017. p. 541-553 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10118 LNCS).

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

TY - GEN

T1 - Thermal imaging based elderly fall detection

AU - Vadivelu, Somasundaram

AU - Ganesan, Sudakshin

AU - Murthy, O. V.Ramana

AU - Dhall, Abhinav

N1 - Conference code: 13th

PY - 2017

Y1 - 2017

N2 - Elderly fall detection is very special case of human action recognition from videos and has very practical application in old age home and nursing centers. Fall detection in its simplest form is a binary classification of fall event or other daily routine activities. Hence, the current trend of sophisticated techniques being developed for human action recognition, particularly with scenarios of large number of classes may not be required in elderly fall detection. However, other design considerations such as simplicity (ready to be deployed), privacy issues (not revealing the identity) are to focused and are the major contributions of this paper. The Spatio-Temporal Interest Points (STIP) and Fisher vector framework for human action recognition is established as baseline in this work. A novel optical flow based technique is proposed that yields better performance than the baseline. Further, a very economical thermal imaging based input modality is proposed. Along with the thermal images not revealing the identity of the persons, thermal images also aid human detection from backgrounds – a useful solution in computing the optical flow of human movements. The proposed solution is also validated on the KUL Simulated Fall dataset showing its generalization capability.

AB - Elderly fall detection is very special case of human action recognition from videos and has very practical application in old age home and nursing centers. Fall detection in its simplest form is a binary classification of fall event or other daily routine activities. Hence, the current trend of sophisticated techniques being developed for human action recognition, particularly with scenarios of large number of classes may not be required in elderly fall detection. However, other design considerations such as simplicity (ready to be deployed), privacy issues (not revealing the identity) are to focused and are the major contributions of this paper. The Spatio-Temporal Interest Points (STIP) and Fisher vector framework for human action recognition is established as baseline in this work. A novel optical flow based technique is proposed that yields better performance than the baseline. Further, a very economical thermal imaging based input modality is proposed. Along with the thermal images not revealing the identity of the persons, thermal images also aid human detection from backgrounds – a useful solution in computing the optical flow of human movements. The proposed solution is also validated on the KUL Simulated Fall dataset showing its generalization capability.

KW - Optical flow

KW - Gaussian Mixture Model (GMM)

KW - Interest point

KW - Video segment

KW - Fall detection

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DO - 10.1007/978-3-319-54526-4_40

M3 - Conference contribution

AN - SCOPUS:85016112302

SN - 9783319545257

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 541

EP - 553

BT - Computer Vision - ACCV 2016 Workshops

A2 - Chen, Chu-Song

A2 - Ma, Kai-Kuang

A2 - Lu, Jiwen

PB - Springer Verlag

T2 - 13th Asian Conference on Computer Vision

Y2 - 20 November 2016 through 24 November 2016

ER -

Vadivelu S, Ganesan S, Murthy OVR, Dhall A. Thermal imaging based elderly fall detection. In Chen CS, Ma KK, Lu J, editors, Computer Vision - ACCV 2016 Workshops: ACCV 2016 International Workshops, Revised Selected Papers. Springer Verlag. 2017. p. 541-553. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-54526-4_40

Thermal imaging based elderly fall detection

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Keywords

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