Usefulness of Background Removal for Video Anomaly Detection Using Generative Adversarial Networks
Kentaro Tsukagoshi, Satoshi Hashimoto, Kazunori Umeda · The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2021
Research on anomaly detection has been conducted to capture anomalies in various situations in daily life. In this study, we aim to build a more accurate anomaly detection system that can detect people and cars behaving abnormally in the video. The background information is considered to be unnecessary for anomaly detection. Therefore, we propose a method to multiply the difference between the generated image and the true value of the optical flow by the image with background removal. We confirm that detection results with high accuracy are produced compared with the conventional method.