Papers › Generative Neural Networks for Anomaly Detection in Crowded Scenes
Generative Neural Networks for Anomaly Detection in Crowded Scenes
Tian Wang, Meina Qiao, Zhiwei Lin, Ce Li, Hichem Snoussi, Zhe Liu, Chang Choi
Security surveillance is critical to social harmony and people's peaceful life. It has a great impact on strengthening social stability and life safeguarding. Detecting anomaly timely, effectively and efficiently in video surveillance remains challenging. This paper proposes a new approach, called S 2 -VAE, for anomaly detection from video data. The S 2 -VAE consists of two proposed neural networks: a Stacked Fully Connected Variational AutoEncoder (S F -VAE) and a Skip Convolutional VAE (S C -VAE). The S F -VAE is a shallow generative network to obtain a model like Gaussian mixture to fit the distribution of the actual data. The S C -VAE, as a key component of S 2 -VAE, is a deep generative network to take advantages of CNN, VAE and skip connections. Both S F -VAE and S C -VAE are efficient and effective generative networks and they can achieve better performance for detecting both local abnormal events and global abnormal events. The proposed S 2 -VAE is evaluated using four public datasets. The experimental results show that the S 2 -VAE outperforms the state-of-the-art algorithms. The code is available publicly at https://github.com/tianwangbuaa/.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Abnormal Event Detection In Video | UBI-Fights | s2-VAE | AUC | 0.610 | #3 of 6 | Archive leaderboard | report |
| Abnormal Event Detection In Video | UBI-Fights | s2-VAE | Decidability | 0.323 | #3 of 6 | Archive leaderboard | report |
| Abnormal Event Detection In Video | UBI-Fights | s2-VAE | EER | 0.427 | #3 of 6 | Archive leaderboard | report |
| Semi-supervised Anomaly Detection | UBI-Fights | s2-VAE | AUC | 0.540 | #5 of 7 | Archive leaderboard | report |
| Semi-supervised Anomaly Detection | UBI-Fights | s2-VAE | Decidability | 0.164 | #5 of 7 | Archive leaderboard | report |
| Semi-supervised Anomaly Detection | UBI-Fights | s2-VAE | EER | 0.475 | #5 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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