Human Falling Detection by Anomaly Detection with Auto-Encoder

• Main Authors: Ruei-Kai Cheng, 程瑞凱
• Other Authors: Shyh-Kang Jeng
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/x5w272

碩士 === 國立臺灣大學 === 電信工程學研究所 === 106 === Elderly living alone without family care which usually cause some accident. In this thesis we focus on detect fall event. We propose an unsupervised Auto-Encoder model for this task. Mainly through the home surveillance cameras to get images of the elder’s home activity. Then our falling detection model will analysis the video clip to detect the current situation belong to normal activity or fall event. In the experiment, the fall event and daily event can be perfectly distinguished under the best condition. We compare with other similar architectures in same experiment, our model also gets the best performance. Therefore, we propose a solution for the falling detection task.