# STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection

arXiv: https://arxiv.org/abs/1804.08381 · HTML: https://mlsanity.com/paper/1804.08381/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 17 of 17)

- [Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection](https://mlsanity.com/paper/2111.09099/) (2021)
- [UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection](https://mlsanity.com/paper/2111.08644/) (2021)
- [Learning Not to Reconstruct Anomalies](https://mlsanity.com/paper/2110.09742/) (2021)
- [A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos](https://mlsanity.com/paper/2112.04294/) (2021)
- [MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection](https://mlsanity.com/paper/2403.14497/) (2024)
- [FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation](https://mlsanity.com/paper/2106.08613/) (2021)
- [OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection](https://mlsanity.com/paper/2103.14953/) (2021)
- [Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies](https://mlsanity.com/paper/2203.13716/) (2022)
- [Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach](https://mlsanity.com/paper/2311.16514/) (2023)
- [PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies](https://mlsanity.com/paper/2303.10704/) (2023)
- [Exploiting Spatial-temporal Correlations for Video Anomaly Detection](https://mlsanity.com/paper/2211.00829/) (2022)
- [LGN-Net: Local-Global Normality Network for Video Anomaly Detection](https://mlsanity.com/paper/2211.07454/) (2022)
- [Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies](https://mlsanity.com/paper/2103.10502/) (2021)
- [RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection](https://mlsanity.com/paper/2202.06256/) (2022)
- [Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly Detection](https://mlsanity.com/paper/2412.17210/) (2024)
- [Anomaly Detection with Adversarially Learned Perturbations of Latent Space](https://mlsanity.com/paper/2207.01106/) (2022)
- [Augment to Detect Anomalies with Continuous Labelling](https://mlsanity.com/paper/2207.01112/) (2022)

## Abstract (arXiv)

In this paper, we propose a novel abnormal event detection method with spatio-temporal adversarial networks (STAN). We devise a spatio-temporal generator which synthesizes an inter-frame by considering spatio-temporal characteristics with bidirectional ConvLSTM. A proposed spatio-temporal discriminator determines whether an input sequence is real-normal or not with 3D convolutional layers. These two networks are trained in an adversarial way to effectively encode spatio-temporal features of normal patterns. After the learning, the generator and the discriminator can be independently used as detectors, and deviations from the learned normal patterns are detected as abnormalities. Experimental results show that the proposed method achieved competitive performance compared to the state-of-the-art methods. Further, for the interpretation, we visualize the location of abnormal events detected by the proposed networks using a generator loss and discriminator gradients.