  Frame reconstruction (current or future frame) based on Auto-Encoder (AE) is
a popular method for video anomaly detection. With models trained on the normal
data, the reconstruction errors of anomalous scenes are usually much larger
than those of normal ones. Previous methods introduced the memory bank into AE,
for encoding diverse normal patterns across the training videos. However, they
are memory-consuming and cannot cope with unseen new scenarios in the testing
data. In this work, we propose a dynamic prototype unit (DPU) to encode the
normal dynamics as prototypes in real time, free from extra memory cost. In
addition, we introduce meta-learning to our DPU to form a novel few-shot
normalcy learner, namely Meta-Prototype Unit (MPU). It enables the fast
adaption capability on new scenes by only consuming a few iterations of update.
Extensive experiments are conducted on various benchmarks. The superior
performance over the state-of-the-art demonstrates the effectiveness of our
method.
