Learnable Locality-Sensitive Hashing for Video Anomaly Detection

by   Yue Lu, et al.

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.


Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection

Video anomaly detection aims to find the events in a video that do not c...

Mean-Shifted Contrastive Loss for Anomaly Detection

Deep anomaly detection methods learn representations that separate betwe...

Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection

Anomaly detection aims at identifying deviant samples from the normal da...

Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation

Reconstruction method based on the memory module for visual anomaly dete...

Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups

Anomaly detection is one of the frequent and important subroutines deplo...

G2D: Generate to Detect Anomaly

In this paper, we propose a novel method for irregularity detection. Pre...

Please sign up or login with your details

Forgot password? Click here to reset