Silhouette Orientation Volumes for Efficient Fall Detection in Depth Videos - Akagunduz, Erdem; Aslan, Muzaffer; Sengur, Abdulkadir; Wang, Haibo; Ince, Melih Cevdet (Ieee-Inst Electrical Electronics Engineers Inc, 2017)
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 Naive Bayes classifier. Experiments on the public SDU-Fall dataset show that this new approach achieves 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 six-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.
Keywords: Bag of words; fall detection; naive Bayes classifier; SDU-fall dataset; shape matching; silhouette orientation volume; weizmann action dataset