| G. Goudelis, G. Tsatiris, K. Karpouzis, S. Kollias |
| Fall detection using History Triple Features |
| PETRA 2015, Affective Computing for Biological Activity Recognition in Assistive Environments Workshop, Corfu, 1-3 July 2015 |
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ABSTRACT
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| Accurate identi cation and timely handling of involuntary events, such as falls, plays a crucial part in e ffective assistive environment systems. Fall detection, in particular, is quite critical, especially in households of lonely elderly people. However, the task of visually identifying a fall is challenging as there is a variety of daily activities that can be mistakenly characterized as falls. To tackle this issue, various feature extraction methods that aim to eff ectively distinguish unintentional falls from other everyday activities have been proposed. In this study, we examine the capability of the History Triple Features technique based on Trace transform, to provide noise robust and invariant to diff erent variations features for the spatiotemporal representation of fall occurrences. The aim is to e ectively detect falls among other household-related activities that usually take place indoors. For the evaluation of the algorithm the video sequences from two realistic fall detection datasets of diff erent nature have been used. One is constructed using a ceiling mounted depth camera and the other is constructed using an RGB camera placed on arbitrary positions in different rooms. After forming the feature vectors, we train a support vector machine using a radial basis function kernel. Results show a very good response of the algorithm achieving 100% on both datasets indicating the suitability of the technique to the speci c task.
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| 03 July , 2015 |
| G. Goudelis, G. Tsatiris, K. Karpouzis, S. Kollias, "Fall detection using History Triple Features", PETRA 2015, Affective Computing for Biological Activity Recognition in Assistive Environments Workshop, Corfu, 1-3 July 2015 |
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