Author:
Theresa Mathew Annette,Shirley Stalin Taniya,Sudheer Kumar Krishna,Santhosh Abhinav,Juby Ashwin
Abstract
The elderly population represents a significant and rapidly expanding demographic, with a majority experiencing frequent daily accidents, notably falls. Falls rank as the second leading cause of accidental injury deaths globally. To address this issue, we propose a video classification system designed specifically for fall detection. Our fall detection framework comprises two key steps: firstly, the detection of human posture within video frames, followed by fall classification using Convolutional Neural Networks (CNNs). Additionally, we introduce a novel approach for boundary detection, utilizing object detection techniques beyond a predefined line of surveillance captured by a single camera. Through this integrated methodology, we aim to enhance fall detection and boundary breach detection capabilities, thereby contributing to the advancement of elderly care and safety. (Abstract).
Publisher
International Journal of Innovative Science and Research Technology
Cited by
2 articles.
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