Anomaly Detection in Aerial Videos Via Future Frame Prediction Networks

By the virtue of high flexibility, low-cost, real-time, and high-resolution data acquisition capacity, unmanned aerial vehicles (UAVs) can be exploited for a wide range of applications, especially in surveillance, inspection, and search fields. Such applications aim to detect potential suspicious events, violent human actions from an untrimmed and lengthy UAV video. Anomaly detection methods are highly in demand because it is unrealistic for human experts to manually detect all abnormal events in image scene. However, anomaly detection methods in aerial videos are rarely studied in the remote sensing community. In this paper, We propose a future frame prediction network based on convolutional variational autoencoder networks to detect anomalous events. Compared to several models, our network has a superior performance.