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.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Miao Yu, Syed Mohsen Naqvi, Adel Rhuma, Jonathon A. Chambers
• Keywords: Class Support Vector, ICASSP 2011, Robust Fall Detection, Signal Processing, Support Vector Machine