Fall detection of elder people on thermal imaging using angular normalization, PCA, and weighted K-NN
Abstract
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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References
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