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Conference Papers Year : 2021

A generic interpretable fall detection framework based on low-resolution thermal images

Abstract

In this paper, we addressed the particularly challenging problem of fall detection using very low resolution thermal images. We proposed a new method for fall detection only based on the matches and a determined threshold. By classifying a pair of matched points on the ground or not on the ground, we could easily determine how many percent of the shape of a person is on the ground. Thus, we could determine if there is a fall or not. The experiments show that the method is able to classify features of human silhouette as one the ground or not on the ground.
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Dates and versions

hal-03694835 , version 1 (14-06-2022)

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Yannick Wend Kuni Zoetgnande, Jean-Louis Dillenseger. A generic interpretable fall detection framework based on low-resolution thermal images. 4th edition of the Computer Science Research Days (JRI 2021), Nov 2021, Bobo-Dioulasso, Burkina Faso. ⟨10.4108/eai.11-11-2021.2317972⟩. ⟨hal-03694835⟩
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