  In this paper, we propose $\text{HF}^2$-VAD, a Hybrid framework that
integrates Flow reconstruction and Frame prediction seamlessly to handle Video
Anomaly Detection. Firstly, we design the network of ML-MemAE-SC (Multi-Level
Memory modules in an Autoencoder with Skip Connections) to memorize normal
patterns for optical flow reconstruction so that abnormal events can be
sensitively identified with larger flow reconstruction errors. More
importantly, conditioned on the reconstructed flows, we then employ a
Conditional Variational Autoencoder (CVAE), which captures the high correlation
between video frame and optical flow, to predict the next frame given several
previous frames. By CVAE, the quality of flow reconstruction essentially
influences that of frame prediction. Therefore, poorly reconstructed optical
flows of abnormal events further deteriorate the quality of the final predicted
future frame, making the anomalies more detectable. Experimental results
demonstrate the effectiveness of the proposed method. Code is available at
\href{https://github.com/LiUzHiAn/hf2vad}{https://github.com/LiUzHiAn/hf2vad}.
