# Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds

arXiv: https://arxiv.org/abs/1706.07680 · HTML: https://mlsanity.com/paper/1706.07680/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2017.

## Built on by (most cited first, 28 of 28)

- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection](https://mlsanity.com/paper/2303.13051/) (2023)
- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network](https://mlsanity.com/paper/2211.00882/) (2022)
- [Networking Systems for Video Anomaly Detection: A Tutorial and Survey](https://mlsanity.com/paper/2405.10347/) (2024)
- [Reconstruction Student with Attention for Student-Teacher Pyramid Matching](https://mlsanity.com/paper/2111.15376/) (2021)
- [Constrained unsupervised anomaly segmentation](https://mlsanity.com/paper/2203.01671/) (2022)
- [CHAD: Charlotte Anomaly Dataset](https://mlsanity.com/paper/2212.09258/) (2022)
- [FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation](https://mlsanity.com/paper/2106.08613/) (2021)
- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (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)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [MemStream: Memory-Based Streaming Anomaly Detection](https://mlsanity.com/paper/2106.03837/) (2021)
- [Discrete neural representations for explainable anomaly detection](https://mlsanity.com/paper/2112.05585/) (2021)
- [Privacy-Preserving Video Anomaly Detection: A Survey](https://mlsanity.com/paper/2411.14565/) (2024)
- [X-MAN: Explaining multiple sources of anomalies in video](https://mlsanity.com/paper/2106.08856/) (2021)
- [Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems](https://mlsanity.com/paper/2204.03141/) (2022)
- [A Modular and Unified Framework for Detecting and Localizing Video Anomalies](https://mlsanity.com/paper/2103.11299/) (2021)
- [Looking at the whole picture: constrained unsupervised anomaly segmentation](https://mlsanity.com/paper/2109.00482/) (2021)
- [The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM](https://mlsanity.com/paper/2507.21649/) (2025)
- [Transfer learning for cross-modal demand prediction of bike-share and public transit](https://mlsanity.com/paper/2203.09279/) (2022)
- [RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection](https://mlsanity.com/paper/2202.06256/) (2022)
- [Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection](https://mlsanity.com/paper/2103.00684/) (2021)
- [Generalized One-Class Learning Using Pairs of Complementary Classifiers](https://mlsanity.com/paper/2106.13272/) (2021)
- [Anomaly detection using prediction error with Spatio-Temporal Convolutional LSTM](https://mlsanity.com/paper/2205.08812/) (2022)
- [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)

Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios. However, the ambiguity and the lack of sufficient abnormal ground truth data makes end-to-end training of large deep networks hard in this domain. In this paper we propose to use Generative Adversarial Nets (GANs), which are trained to generate only the normal distribution of the data. During the adversarial GAN training, a discriminator (D) is used as a supervisor for the generator network (G) and vice versa. At testing time we use D to solve our discriminative task (abnormality detection), where D has been trained without the need of manually-annotated abnormal data. Moreover, in order to prevent G learn a trivial identity function, we use a cross-channel approach, forcing G to transform raw-pixel data in motion information and vice versa. The quantitative results on standard benchmarks show that our method outperforms previous state-of-the-art methods in both the frame-level and the pixel-level evaluation.