Papers › VALD-GAN: video anomaly detection using latent discriminator augmented GAN
VALD-GAN: video anomaly detection using latent discriminator augmented GAN
Rituraj Singh, Anikeit Sethi, Krishanu Saini, Sumeet Saurav, Aruna Tiwari, Sanjay Singh
The most crucial and difficult challenge for intelligent video surveillance is to identify anomalies in a video that comprises anomalous behavior or occurrences. The ambiguous definition of the anomaly makes the detection of it a challenging task. Inspired by the wide adoption of generative adversarial networks (GANs), we proposed video anomaly detection using latent discriminator augmented GAN (VALD-GAN), which combines the representation power of GANs with a novel latent discriminator framework to make the latent space follow a pre-defined distribution. We show through our experimental results that the proposed method significantly increases the anomaly discrimination capability of the model. VALD-GAN achieves an AUC and EER score of 97.98, 6.0% on UCSD Peds1, 97.74, 7.01% on UCSD Peds2, and 91.03, 9.04% on CUHK Avenue dataset, respectively. Also, it is able to detect 62 out of a total of 66 anomalous events with 4 as false alarms and 19 out of a total of 19 with 1 false alarm from Subway Entrance and Exit video datasets, respectively.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | CUHK Avenue | VALD-GAN | AUC | 91.03 | #16 of 35 | Archive leaderboard | report |
| Anomaly Detection | UCSD Ped2 | VALD-GAN | AUC | 97.74 | #8 of 14 | 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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