Multi-level Memory-augmented Appearance-Motion Correspondence Framework for Video Anomaly Detection

no code implementations • 9 Mar 2023 • Xiangyu Huang, Caidan Zhao, Jinghui Yu, Chenxing Gao, Zhiqiang Wu

Frame prediction based on AutoEncoder plays a significant role in unsupervised video anomaly detection.

Anomaly Detection Video Anomaly Detection

Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder

no code implementations • 9 Mar 2023 • Xiangyu Huang, Caidan Zhao, Chenxing Gao, Lvdong Chen, Zhiqiang Wu

To alleviate this issue, we propose a simple yet efficient framework for video anomaly detection.

One-Class Classification Video Anomaly Detection

Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency

no code implementations • 9 Mar 2023 • Xiangyu Huang, Caidan Zhao, Zhiqiang Wu

To address this issue, we propose a framework of Appearance-Motion Semantics Representation Consistency that uses the gap of appearance and motion semantic representation consistency between normal and abnormal data.

Anomaly Detection Video Anomaly Detection

A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency

no code implementations • 8 Apr 2022 • Xiangyu Huang, Caidan Zhao, Yilin Wang, Zhiqiang Wu

Firstly, we design a two-stream encoder to encode the appearance and motion information representations of normal samples and introduce constraints to further enhance the consistency of the feature semantics between appearance and motion information of normal samples so that abnormal samples with low consistency appearance and motion feature representation can be identified.

Anomaly Detection Optical Flow Estimation +1