The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Activity Summarisation and Fall Detection in a Supportive Home Environment
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Assessing Temporal Coherence for Posture Classification with Large Occlusions
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
A Simple and Fast Surveillance System for Human Tracking and Behavior Analysis
SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
Robust Fall Detection Using Human Shape and Multi-class Support Vector Machine
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Multiple object tracking for fall detection in real-time surveillance system
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 3
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Automatic detection of human fall in video
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
HMM based falling person detection using both audio and video
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
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In the next few decades the increase of the number of elderly people will be of major concern, so that solutions must be found in order to maintain them at home. However such a population is exposed to the risk of falls, that can lead to dependency. This paper recalls some approaches used for fall detection and focuses on a method based on an uncalibrated camera. Motion detection uses a combination of simple Gaussian background modelling and interframe difference for person shape detection and features extraction. These features feed a Hidden Markov Model dedicated to fall detection. The algorithm has been tested on real data and we show that simple techniques can be used in order to obtain a fast and reliable fall detection system.