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Fall detection for the elderly in a smart room by using an enhanced one class support vector machine

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4 Author(s)
Miao Yu ; Advanced Signal Processing Group, Electronic and Electrical Engineering Department, Loughborough University, Leicester, UK ; Adel Rhuma ; Syed Mohsen Naqvi ; Jonathon Chambers

In this paper, we propose a novel and robust fall detection system by using a one class support vector machine based on video information. Video features, including the differences of centroid position and orientation of a voxel person over a time interval are extracted from multiple cameras. A one class support vector machine (OCSVM) is used to distinguish falls from other activities, such as walking, sitting, standing, bending or lying. Unlike the conventional OCSVM which only uses the target samples corresponding to falls for training, some non-fall samples are also used to train an enhanced OCSVM with a more accurate decision boundary. From real video sequences, the success of the method is confirmed, that is, by adding a certain number of negative samples, both high true positive detection rate and low false positive detection rate can be obtained.

Published in:

2011 17th International Conference on Digital Signal Processing (DSP)

Date of Conference:

6-8 July 2011