Study of one class boundary method classifiers for application in a video-based fall detection system

Yu, Miao and Naqvi, S. M. and Rhuma, A. and Chambers, J. (2012) Study of one class boundary method classifiers for application in a video-based fall detection system. IET Computer Vision, 6 (2). pp. 90-100. ISSN 1751-9632

Full content URL: http://dx.doi.org/10.1049/iet-cvi.2011.0046

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Abstract

In this paper, we introduce a video-based robust fall detection system for monitoring an elderly person in a smart room environment. Video features, namely the centroid and orientation of a voxel person, are extracted. The boundary method, which is an example one class classification technique, is then used to determine whether the incoming features lie in the ‘fall region’ of the feature space, and thereby effectively distinguishing a fall from other activities, such as walking, sitting, standing, crouching or lying. Four different types of boundary methods, k-center, k-th nearest neighbor, one class support vector machine and single class minimax probability machine are assessed on representative test datasets. The comparison is made on the following three aspects: 1). True positive rate, false positive rate and geometric means in detection 2). Robustness to noise in the training dataset 3). The computational time for the test phase. From the comparison results, we show that the single class minimax probability machine achieves the best overall performance. By applying one class classification techniques with 3-d features, we can obtain a more efficient fall detection system with acceptable performance, as shown in the experimental part; besides, it can avoid the drawbacks of other traditional fall detection methods.

Keywords:voxel person, boundary method, fall detection, one class classification
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:26777
Deposited On:22 Mar 2017 15:55

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