Fall detection of elder people on thermal imaging using angular normalization, PCA, and weighted K-NN
In this paper we design an algorithm capable to classifying thermal images of people lying down and stand, to be applying it to a fall detection system. An automatic rotation, translation and size normalization algorithm was designed, to be applied of thermal image database, with the purpose of obta...
| Autores: | , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | México |
| Recursos: | UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO |
| Repositorio: | PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI |
| Idioma: | español |
| OAI Identifier: | oai:repository.uaeh.edu.mx:article/9344 |
| Acesso em linha: | https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/9344 |
| Access Level: | acceso abierto |
| Palavra-chave: | Pattern recognition PCA Weighted K-NN Image registration Fall detection Reconocimiento de patrones K-NN ponderado Registro de imágenes Detección de caídas |
| Resumo: | In this paper we design an algorithm capable to classifying thermal images of people lying down and stand, to be applying it to a fall detection system. An automatic rotation, translation and size normalization algorithm was designed, to be applied of thermal image database, with the purpose of obtaining a new set of aligned images to performing a dimensionality reduction using PCA. Sequences of 100 frames were used, and both falling and non-falling sequences were produced. Applying the weighted K-NN classifier to identify the class of each frame, a probability vector of the lying class with 100 positions was obtained. These new vectors were used as training examples for a new K-NN classifier, which contains examples of falling and non-falling probability vectors. By applying cross-validation, the system is capable of recognizing falls with 91 % accuracy. |
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