Real time video processing for detecting unexpected behaviours

Unexpected behaviours are human actions that are considered out of the norm. In this paper, the unexpected behaviours that will be focused on is fall. Fall is a common problem especially for elderly. A fall can result in serious injury, permanent disability or even death. However, the consequences o...

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Main Author: Yong, Chun Yee
Other Authors: Kai-Kuang Ma
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/139310
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1393102023-07-07T18:28:10Z Real time video processing for detecting unexpected behaviours Yong, Chun Yee Kai-Kuang Ma School of Electrical and Electronic Engineering ekkma@ntu.edu.sg Engineering::Electrical and electronic engineering Unexpected behaviours are human actions that are considered out of the norm. In this paper, the unexpected behaviours that will be focused on is fall. Fall is a common problem especially for elderly. A fall can result in serious injury, permanent disability or even death. However, the consequences of fall can be largely mitigated if the fall is detected soon enough. Hence, fall detection devices are developed to detect fall and alert relevant people of the incident. Fall detection devices are categorized into three types; wearable-based, ambient-based and vision-based fall detector. The proposed system is developed using vision-based approach. Body shape change and bounding box techniques are utilized to develop the fall detection algorithm on Python and OpenCV. Telegram Bot Platform is integrated into the algorithm as part of the alert system for the device. Fall simulations conducted on the algorithm yield an accuracy of 83% in detecting falls. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-18T21:16:18Z 2020-05-18T21:16:18Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/139310 en A3146-191 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Yong, Chun Yee
Real time video processing for detecting unexpected behaviours
description Unexpected behaviours are human actions that are considered out of the norm. In this paper, the unexpected behaviours that will be focused on is fall. Fall is a common problem especially for elderly. A fall can result in serious injury, permanent disability or even death. However, the consequences of fall can be largely mitigated if the fall is detected soon enough. Hence, fall detection devices are developed to detect fall and alert relevant people of the incident. Fall detection devices are categorized into three types; wearable-based, ambient-based and vision-based fall detector. The proposed system is developed using vision-based approach. Body shape change and bounding box techniques are utilized to develop the fall detection algorithm on Python and OpenCV. Telegram Bot Platform is integrated into the algorithm as part of the alert system for the device. Fall simulations conducted on the algorithm yield an accuracy of 83% in detecting falls.
author2 Kai-Kuang Ma
author_facet Kai-Kuang Ma
Yong, Chun Yee
format Final Year Project
author Yong, Chun Yee
author_sort Yong, Chun Yee
title Real time video processing for detecting unexpected behaviours
title_short Real time video processing for detecting unexpected behaviours
title_full Real time video processing for detecting unexpected behaviours
title_fullStr Real time video processing for detecting unexpected behaviours
title_full_unstemmed Real time video processing for detecting unexpected behaviours
title_sort real time video processing for detecting unexpected behaviours
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/139310
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