  Video anomaly detection (VAD) mainly refers to identifying anomalous events
that have not occurred in the training set where only normal samples are
available. Existing works usually formulate VAD as a reconstruction or
prediction problem. However, the adaptability and scalability of these methods
are limited. In this paper, we propose a novel distance-based VAD method to
take advantage of all the available normal data efficiently and flexibly. In
our method, the smaller the distance between a testing sample and normal
samples, the higher the probability that the testing sample is normal.
Specifically, we propose to use locality-sensitive hashing (LSH) to map samples
whose similarity exceeds a certain threshold into the same bucket in advance.
In this manner, the complexity of near neighbor search is cut down
significantly. To make the samples that are semantically similar get closer and
samples not similar get further apart, we propose a novel learnable version of
LSH that embeds LSH into a neural network and optimizes the hash functions with
contrastive learning strategy. The proposed method is robust to data imbalance
and can handle the large intra-class variations in normal data flexibly.
Besides, it has a good ability of scalability. Extensive experiments
demonstrate the superiority of our method, which achieves new state-of-the-art
results on VAD benchmarks.
