@inproceedings{6d689a5d1c174420b7e78f5d8ac63f2e,
title = "HMM based falling person detection using both audio and video",
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.",
author = "\{Uǧur T{\"o}reyin\}, B. and Yiǧithan Dedeoǧlu and \{Enis {\c C}etin\}, A.",
year = "2005",
language = "English",
isbn = "3540296204",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "211--220",
booktitle = "Computer Vision in Human-Computer Interaction - ICCV 2005 Workshop on HCI, Proceedings",
note = "ICCV 2005 Workshop on HCI - Computer Vision in Human-Computer Interaction ; Conference date: 21-10-2005 Through 21-10-2005",
}