Person Anomaly Detection based on Autoencoder with Obrid-Sensor
Taiki Sunakawa, Yuki Horikawa, Atsushi Matsubara, Seiji Nishifuji, Shota Nakashima · 2021
This paper explores a novel method of fall detection assuming elderly people which can be trained easily by using AutoEncoder. The classifier has accuracy is 98.7%, which is 2.1 points higher than conventional method. In this method, Obrid-Sensor acquire brightness information. Moreover, the information based to detect whether a person is in a falling state with protecting privacy. On the other hand, the conventional method uses a classifier built by Support Vector Machine for fall detection. However it is necessary to prepare the data of the falling state as well as the standing state for training. In the proposed method, 78% less required training data than the conventional method, and only use the data of standing state for training.