The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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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Falls are a great risk for elderly people living alone. Falls can result in serious injuries and in some cases even deaths. It is important to recognize them early and provide assistance. In this paper we present a novel computer vision based fall recognition system which combines depth map with normal color information. With this combination it is possible to achieve better results as depth map reduces many errors and gives more information about the scene. We track and extract motion from the depth as well RGB map and then use Support Vector Machines to classify the falls. Our proposed fall recognition system recognizes and classifies falls from other actions with a very high accuracy (greater than 95%).