Papers › STemGAN: spatio-temporal generative adversarial network for video anomaly detection

STemGAN: spatio-temporal generative adversarial network for video anomaly detection

22 Sep 2023Applied Intelligence 2023 9archive 2025-07-28

Rituraj Singh, Krishanu Saini, Anikeit Sethi, Sumeet Saurav, Aruna Tiwari, Sanjay Singh

Automatic detection and interpretation of abnormal events have become crucial tasks in large-scale video surveillance systems. The challenges arise from the lack of a clear definition of abnormality, which restricts the usage of supervised methods. To this end, we propose a novel unsupervised anomaly detection method, Spatio-Temporal Generative Adversarial Network (STemGAN). This framework consists of a generator and discriminator that learns from the video context, utilizing both spatial and temporal information to predict future frames. The generator follows an Autoencoder (AE) architecture, having a dual-stream encoder for extracting appearance and motion information, and a decoder having a Channel Attention (CA) module to focus on dynamic foreground features. In addition, we provide a transfer-learning method that enhances the generalizability of STemGAN. We use benchmark Anomaly Detection (AD) datasets to compare the performance of our approach with the existing state-of-the-art approaches using standard evaluation metrics, i.e., AUC (Area Under Curve) and EER (Equal Error Rate). The empirical results show that our proposed STemGAN outperforms the existing state-of-the-art methods achieving an AUC score of 97.5% on UCSDPed2, 86.0% on CUHK Avenue, 90.4% on Subway-entrance, and 95.2% on Subway-exit.

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Tasks

Anomaly Detection Anomaly Detection In Surveillance Videos Decoder Transfer Learning Unsupervised Anomaly Detection Video Anomaly Detection

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Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Anomaly Detection	CUHK Avenue	STemGAN	AUC	86.0	#29 of 35	Archive leaderboard	report
Anomaly Detection	UCSD Ped2	STemGAN	AUC	97.5	#9 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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