Bi-directional Frame Interpolation for Unsupervised Video Anomaly Detection

Anomaly detection in video surveillance aims to detect anomalous frames whose properties significantly differ from normal patterns. Anomalies in videos can occur in both spatial appearance and temporal motion, making unsupervised video anomaly detection challenging. To tackle this problem, we investigate forward and backward motion continuity between adjacent frames and propose a new video anomaly detection paradigm based on bi-directional frame interpolation. The proposed framework consists of an optical flow estimation network and an interpolation network jointly optimized end-to-end to synthesize a middle frame from its nearest two frames. We further introduce a novel dynamic memory mechanism to balance memory sparsity and normality representation diversity, which attenuates abnormal features in frame interpolation without affecting normal prototypes. In inference, interpolation error and dynamic memory error are fused as anomaly scores. The proposed bi-directional interpolation design improves normal frame synthesis, lowering the false alarm rate of anomaly appearance; meanwhile, the implicit "regular" motion constraint in our optical flow estimation and the novel dynamic memory mechanism play blocking roles in interpolating abnormal frames, increasing the system's sensitivity to anomalies. Extensive experiments on public benchmarks demonstrates the superiority of the proposed framework over prior arts.