Introducing a statistical behavior model into camera-based fall detection

Authors:
Andreas Zweng;Sebastian Zambanini;Martin Kampel
Affiliations:
Computer Vision Lab, Vienna University of Technology, Vienna, Austria;Computer Vision Lab, Vienna University of Technology, Vienna, Austria;Computer Vision Lab, Vienna University of Technology, Vienna, Austria
Venue:
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Year:
2010

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Abstract

Camera based fall detection represents a solution to the problem of people falling down and being not able to stand up on their own again. For elderly people who live alone, such a fall is a major risk. In this paper we present an approach for fall detection based on multiple cameras supported by a statistical behavior model. The model describes the spatio-temporal unexpectedness of objects in a scene and is used to verify a fall detected by a semantic driven fall detection. In our work a fall is detected using multiple cameras where each of the camera inputs results in a separate fall confidence. These confidences are then combined into an overall decision and verified with the help of the statistical behavior model. This paper describes the fall detection approach as well as the verification step and shows results on 73 video sequences.