Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety

Zi, X; Chaturvedi, K; Braytee, A; Li, J; Prasad, M

Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety

Zi, X Chaturvedi, K Braytee, A Li, J Prasad, M

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Electronics, 12, (5), pp. 1259

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Field	Value	Language
dc.contributor.author	Zi, X
dc.contributor.author	Chaturvedi, K
dc.contributor.author	Braytee, A
dc.contributor.author	Li, J
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.date.accessioned	2023-03-24T07:32:38Z
dc.date.available	2023-03-24T07:32:38Z
dc.identifier.citation	Electronics, 12, (5), pp. 1259
dc.identifier.issn	1450-5843
dc.identifier.issn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10453/168346
dc.description.abstract	<jats:p>Falls are one the leading causes of accidental death for all people, but the elderly are at particularly high risk. Falls are severe issue in the care of those elderly people who live alone and have limited access to health aides and skilled nursing care. Conventional vision-based systems for fall detection are prone to failure in conditions with low illumination. Therefore, an automated system that detects falls in low-light conditions has become an urgent need for protecting vulnerable people. This paper proposes a novel vision-based fall detection system that uses object tracking and image enhancement techniques. The proposed approach is divided into two parts. First, the captured frames are optimized using a dual illumination estimation algorithm. Next, a deep-learning-based tracking framework that includes detection by YOLOv7 and tracking by the Deep SORT algorithm is proposed to perform fall detection. On the Le2i fall and UR fall detection (URFD) datasets, we evaluate the proposed method and demonstrate the effectiveness of fall detection in dark night environments with obstacles.</jats:p>
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Electronics
dc.relation.isbasedon	10.3390/electronics12051259
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety
dc.type	Journal Article
utslib.citation.volume	12
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-24T07:32:23Z
pubs.issue	5
pubs.publication-status	Published online
pubs.volume	12
utslib.citation.issue	5

Abstract:

Falls are one the leading causes of accidental death for all people, but the elderly are at particularly high risk. Falls are severe issue in the care of those elderly people who live alone and have limited access to health aides and skilled nursing care. Conventional vision-based systems for fall detection are prone to failure in conditions with low illumination. Therefore, an automated system that detects falls in low-light conditions has become an urgent need for protecting vulnerable people. This paper proposes a novel vision-based fall detection system that uses object tracking and image enhancement techniques. The proposed approach is divided into two parts. First, the captured frames are optimized using a dual illumination estimation algorithm. Next, a deep-learning-based tracking framework that includes detection by YOLOv7 and tracking by the Deep SORT algorithm is proposed to perform fall detection. On the Le2i fall and UR fall detection (URFD) datasets, we evaluate the proposed method and demonstrate the effectiveness of fall detection in dark night environments with obstacles.

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http://hdl.handle.net/10453/168346