Smart Homes: An occlusion-resistant fall detection system for the elderly
2022 (Engelska)Självständigt arbete på avancerad nivå (masterexamen), 80 poäng / 120 hp
Studentuppsats (Examensarbete)
Abstract [en]
Falls represent one of the most severe issues faced by the elder population. Therefore,in this work, we propose a system for fall detection using RGB camera images. In thisregard, we propose, associated to multiple classifiers, a fusion between features calculatedfrom the head region and those calculated from the whole body’s region. These regionsare extracted using the YOLOv5 object detector. Experiments conducted on a standardfall dataset showed satisfying and promising results. The system based on features fromthe head data yielded an accuracy of 94.79%, while the system based on fusion betweenhead and body features produced an accuracy of 98.08%.
Ort, förlag, år, upplaga, sidor
2022. , s. 62
Nyckelord [en]
fall detection, YOLOv5, SVM, K-NN, RFC, MLP
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:ltu:diva-92684OAI: oai:DiVA.org:ltu-92684DiVA, id: diva2:1690722
Ämne / kurs
Examensarbete, minst 30 hp
Utbildningsprogram
Hållbar datorkommunikation och molnbaserad databehandling, master
Presentation
2022-06-08, Amphi 7, Faculté des Sciences & Technologies, Nancy, France, 15:30 (Engelska)
Handledare
Examinatorer
2022-11-102022-08-262022-11-10Bibliografiskt granskad