Abnormal Human Action Detection Based on GAN
Tomoya Sano, Seiji Ishikawa, Joo Kooi Tan · Proceedings of International Conference on Artificial Life and Robotics · 2021
One of the important roles of a camera surveillance system is to detect abnormal human actions or events.In this study, we propose a method of abnormal human actions/events detection method using Generative Adversarial Nets (GAN).In anomaly action detection, the main problem is that the image data of abnormal human actions is more difficult to obtain than normal human actions.To solve this difficulty, we use only normal human action data in the employed training network and those actions not recognized as normal are judged as abnormal.Experimental results show effectiveness of the proposed method.