@article{ref1,
title="Silhouette orientation volumes for efficient fall detection in depth videos",
journal="IEEE journal of biomedical and health informatics",
year="2016",
author="Akagunduz, Erdem and Aslan, Muzaffer and Sengur, Abdulkadir and Wang, Haibo and Ince, Melih",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="A novel method to detect human falls in depth videos is presented in this paper. A fast and robust shape sequence descriptor, namely the Silhouette Orientation Volume (SOV), is used to represent actions and classify falls. The SOV descriptor provides high classification accuracy even with a combination of simple associated models such as Bag-of-Words and the Naïve Bayes classifier. Experiments on the public SDU-Fall dataset show that this new approach achieves an up-to 91.89% fall detection accuracy with a single-view depth camera. The classification rate is about 5% higher than the results reported in the literature. An overall accuracy of 89.63% was obtained for the 6-class action recognition, which is about 25% higher than the state of the art. Moreover, a perfect silhouette-based action recognition rate of 100% is achieved on the Weizmann action dataset.<p /> <p>Language: en</p>",
language="en",
issn="2168-2194",
doi="10.1109/JBHI.2016.2570300",
url="http://dx.doi.org/10.1109/JBHI.2016.2570300"
}