Anomaly detection in non-stationary videos using time-recursive differencing network based prediction

Most videos, including those captured through aerial remote sensing, are usually nonstationary in nature having time-varying feature statistics. Although sophisticated reconstruction and prediction models exist for video anomaly detection (VAD), effective handling of nonstationarity has seldom been considered explicitly. In this letter, we propose to perform prediction using a time-recursive differencing network followed by autoregressive moving average estimation for VAD. The differencing network is employed to effectively handle nonstationarity in video data during the anomaly detection. Focusing on the prediction process, the effectiveness of the proposed approach is demonstrated considering a simple optical flow-based video feature, and by generating qualitative and quantitative results on three aerial video data sets and two standard anomaly detection video data sets. Equal error rate (EER), area under curve (AUC), and ROC curve-based comparison with several existing methods including the state-of-the-art reveal the superiority of the proposed approach.