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Visual Context-Aware Person Fall Detection
April 15, 2024, 4:44 a.m. | Aleksander Nagaj, Zenjie Li, Dim P. Papadopoulos, Kamal Nasrollahi
cs.CV updates on arXiv.org arxiv.org
Abstract: As the global population ages, the number of fall-related incidents is on the rise. Effective fall detection systems, specifically in healthcare sector, are crucial to mitigate the risks associated with such events. This study evaluates the role of visual context, including background objects, on the accuracy of fall detection classifiers. We present a segmentation pipeline to semi-automatically separate individuals and objects in images. Well-established models like ResNet-18, EfficientNetV2-S, and Swin-Small are trained and evaluated. During …
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