| Texto completo | |
| Autor(es): Mostrar menos - |
Carneiro, Sarah Almeida
;
da Silva, Gabriel Pellegrino
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Leite, Guilherme Vieira
;
Moreno, Ricardo
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Guimaraes, Silvio Jamil F.
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Pedrini, Helio
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RimacDrlje, S
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Zagar, D
;
Galic, I
;
Martinovic, G
;
Vranjes, D
;
Habijan, M
Número total de Autores: 12
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| Tipo de documento: | Artigo Científico |
| Fonte: | PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP 2019); v. N/A, p. 6-pg., 2019-01-01. |
| Resumo | |
Sporadic falls, due to the lack of balance and other factors, are some of the complications that elderly people might experience more frequently than others. Accordingly, as there is a high probability of these events causing major health casualties, such as bone breaking or head clots, studies have been monitoring these falls to rapidly assist the victim. In this work, we propose and evaluate a multi-stream learning model based on convolutional neural networks using high-level handcrafted features as input in order to cope with this situation. Therefore, our approach consists of extracting high-level handcrafted features, for instance, human pose estimation and optical flow, and using each one as an input for a distinct VGG-16 classifier. In addition, these experiments are able to showcase what features can be used in fall detection. The results have shown that by assembling our directed input learners, our approach outperforms, in terms of accuracy and sensitivity rates, to other similar tested methods found in literature. (AU) | |
| Processo FAPESP: | 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo? |
| Beneficiário: | Alexandre Xavier Falcão |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade |
| Beneficiário: | Anderson de Rezende Rocha |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |