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

Yu, Miao and Rhuma, Adel and Naqvi, Syed Mohsen and Chambers, Jonathan (2011) Fall detection for the elderly in a smart room by using an enhanced one class support vector machine. In: Digital Signal Processing (DSP), 2011 17th International Conference on, 6 - 8 July 2011, Corfu. Greece.

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FALL DETECTION FOR THE ELDERLY IN A SMART ROOM BY USING AN ENHANCED ONE CLASS SUPPORT VECTOR MACHINE.pdf
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Item Type:Conference or Workshop contribution (Paper)
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Abstract

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.

Keywords:voxel person, multiple cameras, one class support vector machine, fall detection
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G150 Mathematical Modelling
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:26789
Deposited On:30 Mar 2017 13:44

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