NDLI: Visual based fall detection with reduced complexity horprasert segmentation using superpixel

Content Provider	IEEE Xplore Digital Library
Author	Chin-Jou Chong Wooi-Haw Tan Yoong Choon Chang Farid Noor Batcha, M. Karuppiah, E.
Copyright Year	2015
Description	Author affiliation: Fac. of Eng., Multimedia Univ., Cyberjaya, Malaysia (Chin-Jou Chong; Wooi-Haw Tan; Yoong Choon Chang) \|\| Inf. & Commun. Technol., MIMOS Berhad, Kuala Lumpur, Malaysia (Farid Noor Batcha, M.; Karuppiah, E.)
Abstract	Apart from wearable sensors and floor sensors, remote fall detection systems can be realized using camera sensors and computer visions methods and this visual based system is accurate, non-intrusive and capable to perform post fall event analysis with the recorded video. To implement visual based fall detection, the foreground segmentation process is crucial in order to provide the right foreground region with useful features for fall detection and analysis. However, in an indoor environment, change of global illumination, shadow occurrence and colour camouflage tend to occur and affect the performance of foreground extraction. Existing techniques attempted to overcome these issues are compromised with higher computational complexity and longer processing speed. Thus, an approach of using Horprasert algorithm incorporating superpixel clustering is proposed to perform background modeling and background segmentation. The foreground extracted by the proposed method is then tested against two different fall detection methods, using bounding box and motion quantification with approximated ellipse. The result has shown reduction in complexity and improvement in processing speed, without much disparity compared to the original Horprasert segmentation.
Starting Page	462
Ending Page	467
File Size	1117888
Page Count	6
File Format	PDF
ISBN	9781479980697
DOI	10.1109/ICNSC.2015.7116081
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-09
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Image color analysis Lighting Feature extraction Algorithm design and analysis Sensors Hidden Markov models Horprasert Fall detection segmentation superpixel
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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