Title :
Fall detection with depth-videos
Author :
Aslan, Muzaffer ; Alcin, Omer Faruk ; Sengur, Abdulkadir ; Ince, Melih Cevdet
Author_Institution :
Gazi Endustri Meslek Lisesi, Elektron. Bolumu, Elazığ, Turkey
Abstract :
Elderly people, who are living alone are great risk if a fall event occurred. Therefore automatic fall detection systems are in demand for elderly people. In this paper, a depth based fall detection system is proposed. The proposed method consists of shape based fall characterization and a Support Vector Machine (SVM) classifier. Shape based fall characterization is formed with Curvature Scale Space (CSS) features and Fisher Vector (FV) encoding. According to the results obtained from experimental study, the proposed method has better performance than other methods that were published in literature.
Keywords :
computational geometry; geriatrics; image classification; object detection; support vector machines; vectors; CSS features; FV encoding; Fisher vector encoding; SVM classifier; automatic fall detection systems; curvature scale space features; depth-videos; elderly people; shape based fall characterization; support vector machine classifier; Conferences; Encoding; Feature extraction; Informatics; Senior citizens; Shape; Support vector machines; Curvature scale space; Fall decection; Fisher vector; Support vector machines;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
DOI :
10.1109/SIU.2015.7129854