HMM based falling person detection using both audio and video
Date
2005
Authors
Editor(s)
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
BUIR Usage Stats
0
views
views
17
downloads
downloads
Citation Stats
Series
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.
Source Title
Lecture Notes in Computer Science
Publisher
Springer
Course
Other identifiers
Book Title
Keywords
Degree Discipline
Degree Level
Degree Name
Citation
Permalink
Published Version (Please cite this version)
Language
English