A generic interpretable fall detection framework based on low-resolution thermal images
In: 4th edition of the Computer Science Research Days (JRI 2021) ; https://hal.science/hal-03694835 ; 4th edition of the Computer Science Research Days (JRI 2021), Nov 2021, Bobo-Dioulasso, Burkina Faso. ⟨10.4108/eai.11-11-2021.2317972⟩, 2021
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Zugriff:
International audience ; 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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A generic interpretable fall detection framework based on low-resolution thermal images
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| Autor/in / Beteiligte Person: | Zoetgnande, Yannick Wend Kuni ; Dillenseger, Jean-Louis ; Laboratoire Traitement du Signal et de l'Image (LTSI) ; Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM) ; Sere, Abdoulaye ; Marie Yves Théodore Tapsoba ; Borlli Michel Jonas Some ; Sie, Oumarou |
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| Zeitschrift: | 4th edition of the Computer Science Research Days (JRI 2021) ; https://hal.science/hal-03694835 ; 4th edition of the Computer Science Research Days (JRI 2021), Nov 2021, Bobo-Dioulasso, Burkina Faso. ⟨10.4108/eai.11-11-2021.2317972⟩, 2021 |
| Veröffentlichung: | HAL CCSD ; EAI, 2021 |
| Medientyp: | Konferenz |
| DOI: | 10.4108/eai.11-11-2021.2317972 |
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