Unsupervised Anomaly Detection of the First Person in Gait from an Egocentric Camera.

Assistive technology is increasingly important as the senior population grows. The purpose of this study is to develop a means of preventing fatal injury by monitoring the movements of the elderly and sounding an alarm if an accident occurs. We present a method of detecting an anomaly in a first-person’s gait from an egocentric video. Followed by the conventional anomaly detection methods, we train the model in an unsupervised manner. We use optical flow images to capture ego-motion information in the first person. To verify the effectiveness of our model, we introduced and conducted experiments with a novel first-person video anomaly detection dataset and showed that our model outperformed the baseline method.