Publications

Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

This review extends the narrow VAED concept from unsupervised video anomaly detection to Generalized Video Anomaly Event Detection, which provides a comprehensive survey that integrates recent works based on different assumptions and learning frameworks into an intuitive taxonomy.

Yang Liu, Dingkang Yang, Yan Wang, Jing Liu, Jun Liu, Azzedine Boukerche, Peng Sun, Liang Song

Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

Stochastic Video Normality Network for Abnormal Event Detection in Surveillance Videos

The proposed Stochastic Video Normality (SVN) network learns the prototypical local appearance patterns via deterministic multi-task learning and global motion patterns in a non-deterministic manner.

Yang Liu, Dingkang Yang, Gaoyun Fang, Yuzheng Wang, Donglai Wei, Mengyang Zhao, Kai Cheng, Jing Liu, Liang Song

Learning Causality-inspired Representation Consistency for Video Anomaly Detection

We design a causality-inspired representation consistency (CRC) framework to implicitly learn the unobservable causal variables of normality directly from available normal videos and detect abnormal events with the learned representation consistency.

Yang Liu, Zhaoyang Xia, Liang Song

AMP-Net: Appearance-Motion Prototype Network Assisted Automatic Video Anomaly Detection System

We propose an appearance-motion prototype network (AMP-net) that uses external memories to record prototype features and augments the appearance-motion prototype with a spatial-temporal fusion.

Yang Liu, Jing Liu, Liang Song

Distributional and Spatial-Temporal Robust Representation Learning for Transportation Activity Recognition

We introduce a novel parallel model named Distributional and Spatial-Temporal Robust Representation (DSTRR), which combines automatic learning of statistical, spatial, and temporal features into a unified framework.

Jing Liu, Yang Liu, Liang Song