HMM based falling person detection using both audio and video

B. Uǧur Töreyin*, Yiǧithan Dedeoǧlu, A. Enis Çetin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

89 Citations (Scopus)

Abstract

Automatic detection of a falling person in video is an important problem with applications in security and safety areas including supportive home environments and CCTV surveillance systems. Human motion in video is modeled using Hidden Markov Models (HMM) in this paper. In addition, the audio track of the video is also used to distinguish a person simply sitting on a floor from a person stumbling and falling. Most video recording systems have the capability of recording audio as well and the impact sound of a falling person is also available as an additional clue. Audio channel data based decision is also reached using HMMs and fused with results of HMMs modeling the video data to reach a final decision.

Original languageEnglish
Title of host publicationComputer Vision in Human-Computer Interaction - ICCV 2005 Workshop on HCI, Proceedings
Pages211-220
Number of pages10
Publication statusPublished - 2005
Externally publishedYes
EventICCV 2005 Workshop on HCI - Computer Vision in Human-Computer Interaction - Beijing, China
Duration: 21 Oct 200521 Oct 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceICCV 2005 Workshop on HCI - Computer Vision in Human-Computer Interaction
Country/TerritoryChina
CityBeijing
Period21/10/0521/10/05

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