Publication:
Human Fall Detection with Computer Vision and Deep Learning

Simple item page
dc.contributor.authorMuhammad Abid bin Ameren_US
dc.date.accessioned2023-05-03T16:41:11Z
dc.date.available2023-05-03T16:41:11Z
dc.date.issued2020-09
dc.descriptionInterim Semester 2020/2021en_US
dc.description.abstractThis thesis reports on the fall detector using computer vision with deep learning. The main objective of this project is to help reduce the number of a fatal accident due to a fall in an elderly house and hospital. The conventional fall detector usually is not user friendly as it requires the user to wear it all the time. Hence, the fall detector using input from Closed-Circuit Television (CCTV) footage is used to detect fall as it is user friendly and able to be ready to detect fall all the time. A deep learning approach is used in this project to detect a human to reduce false alarm in detection. Thus, making the system to be more reliable and accurate in detecting fall and eventually will reduce the number of a fall accident. Visual studio code and other IDE were used to design and test the system. The accuracy of the system then determined by using fall dataset videosen_US
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21361
dc.language.isoenen_US
dc.subjectFall Detectionen_US
dc.subjectComputer Visionen_US
dc.subjectDeep Learningen_US
dc.titleHuman Fall Detection with Computer Vision and Deep Learningen_US
dspace.entity.typePublication
Files
Collections
College of Engineering (COE)