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ICASSP
2011
IEEE

Fall detection in a smart room by using a fuzzy one class support vector machine and imperfect training data

10 years 9 months ago
Fall detection in a smart room by using a fuzzy one class support vector machine and imperfect training data
In this paper, we propose an efficient and robust fall detection system by using a fuzzy one class support vector machine based on video information. Two cameras are used to capture the video frames from which the features are extracted. A fuzzy one class support vector machine (FOCSVM) is used to distinguish falling from other activities, such as walking, sitting, standing, bending or lying. Compared with the traditional one class support vector machine, the FOCSVM can obtain a more accurate and tight decision boundary under a training dataset with outliers. From real video sequences, the success of the method is confirmed with less non-fall samples being misclassified as falls by the classifier under an imperfect training dataset.
Miao Yu, Syed Mohsen Naqvi, Adel Rhuma, Jonathon A
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Miao Yu, Syed Mohsen Naqvi, Adel Rhuma, Jonathon A. Chambers
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