Silhouette Orientation Volumes for Efficient Fall Detection in Depth Videos
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, cilt.21, sa.3, ss.756-763, 2017 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 21 Sayı: 3
- Basım Tarihi: 2017
- Doi Numarası: 10.1109/jbhi.2016.2570300
- Dergi Adı: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.756-763
- Anahtar Kelimeler: Bag of words, fall detection, naive Bayes classifier, SDU-fall dataset, shape matching, silhouette orientation volume, weizmann action dataset
- Orta Doğu Teknik Üniversitesi Adresli: Hayır
Özet
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.