Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A Smart Sensor to Detect the Falls of the Elderly
IEEE Pervasive Computing
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
Fall Detection from Human Shape and Motion History Using Video Surveillance
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Dynamic Fall Detection and Pace Measurement in Walking Sticks
HCMDSS-MDPNP '07 Proceedings of the 2007 Joint Workshop on High Confidence Medical Devices, Software, and Systems and Medical Device Plug-and-Play Interoperability
Fall Detection and Alert for Ageing-at-Home of Elderly
ICOST '09 Proceedings of the 7th International Conference on Smart Homes and Health Telematics: Ambient Assistive Health and Wellness Management in the Heart of the City
Using wearable sensor and NMF algorithm to realize ambulatory fall detection
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach
IEEE Transactions on Circuits and Systems for Video Technology
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Personalized multimedia alert service of fall event is an important service for ageing in place thanks to it can enhance the life safety of the elderly and then relieve the continuous worrying of the caregivers. This paper presents a personalized alert service of fall event for aging in place based the proposed fall detection algorithm. Our novel algorithm detects falls in sliding windows of video recorded by single fixed camera. The novelty of this algorithm is multiple. First, it acquires 3D information of heads from 2D image locations by looking up a 2D--3D table. Second, it mainly works on sliding windows. In each sliding window, it detects the existences of primitive actions and calculates the measures. Last, it uses a trained SVM to distinguish fall windows from non-fall windows. The inputs to the SVM are the vector consisting of the existences of primitive actions and the calculated measures from sliding windows. The lookup table and measure calculations are achieved with the aid of camera calibration. Experiments show that the proposed algorithm performs well in fall detection. Based on the fall detection we design a personalized multimedia alert service of fall event.