Human Fall Detection with Computer Vision and Deep Learning
Interim Semester 2020/2021
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2023
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my.uniten.dspace-213612023-05-05T05:44:26Z Human Fall Detection with Computer Vision and Deep Learning Muhammad Abid bin Amer Fall Detection Computer Vision Deep Learning Interim Semester 2020/2021 This 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 videos 2023-05-03T16:41:11Z 2023-05-03T16:41:11Z 2020-09 https://irepository.uniten.edu.my/handle/123456789/21361 en application/pdf |
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Fall Detection Computer Vision Deep Learning |
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Fall Detection Computer Vision Deep Learning Muhammad Abid bin Amer Human Fall Detection with Computer Vision and Deep Learning |
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Interim Semester 2020/2021 |
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author |
Muhammad Abid bin Amer |
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Muhammad Abid bin Amer |
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Muhammad Abid bin Amer |
title |
Human Fall Detection with Computer Vision and Deep Learning |
title_short |
Human Fall Detection with Computer Vision and Deep Learning |
title_full |
Human Fall Detection with Computer Vision and Deep Learning |
title_fullStr |
Human Fall Detection with Computer Vision and Deep Learning |
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Human Fall Detection with Computer Vision and Deep Learning |
title_sort |
human fall detection with computer vision and deep learning |
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2023 |
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1806427651703832576 |
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13.222552 |