title: Silhouette Orientation Volumes for Efficient Fall Detection in Depth Videos.
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 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.
authors.forename: Erdem
authors.initials: E
authors.lastname: Akagunduz
authors.forename: Muzaffer
authors.initials: M
authors.lastname: Aslan
authors.forename: Abdulkadir
authors.initials: A
authors.lastname: Sengu
authors.lastname: Haibo Wang
authors.forename: Melih Cevdet
authors.initials: MC
authors.lastname: Ince
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control / Q000706:statistics & numerical data*; D000465:Algorithms; D001499:Bayes Theorem; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D014741:Video Recording