Citation

Akagunduz E, Aslan M, Sengur A, Wang H, Ince M. IEEE J. Biomed. Health Inform. 2016; ePub(ePub): ePub.

Copyright

(Copyright © 2016, Institute of Electrical and Electronics Engineers)

DOI

10.1109/JBHI.2016.2570300

PMID

27214922

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.

Language: en