Transformer-Based Spatio-Temporal Unsupervised Traffic Anomaly Detection in Aerial Videos
Abstract
Anomaly detection is an area of video analysis and plays an increasing role in ensuring safety, preventing risks, and guaranteeing quick response in intelligent surveillance systems. It has become a popular research topic and has piqued the interest of researchers in different communities, such as computer vision, machine learning, remote sensing, and data mining, in recent years. This promotes novel mobile systems where drones are equipped with cameras to help people find better and more efficient solutions to automatically detect anomalies (e.g., car accidents, traffic congestion, street fighting) in traffic surveillance videos. However, anomaly detection methods are still rarely studied and developed in the remote sensing community due to anomalous events rarely occurring in real life, along with the high similarities between the objects of interest with small sizes, multi-scale objects, complex backgrounds of great variations, and high overlap between objects. Therefore, in order to fully exploit the spatio-temporal information for anomaly detection in traffic surveillance circumstances, we propose a future frame prediction network based on transformer architectures to detect abnormal events from drone videography in an unsupervised way. Our model treats consecutive video frames from an input clip and feeds features to a transformer encoder to capture spatial and temporal representations from the sequence. Then, it leverages a decoder to predict the next frame. Furthermore, an event with high reconstruction error is identified as an anomaly in the test phase. Thoroughly empirical studies demonstrate that our method achieves superior performance on the UIT-ADrone dataset and largely outperforms the state-of-the-art anomaly methods on the Drone-Anomaly dataset in aerial surveillance. The source code is available online at https://github.com/Tungufm/ASTT.
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The Transformer Encoder-Decoder framework achieved 82% AUC on aerial traffic videos.
“This framework achieved 82% AUC on aerial traffic videos”
From A Comprehensive Survey of Transformer-Based Models for Video Anomaly Detection in Surveillance Systems (2025) · cites this paper as [21]
Annotation-free anomaly detection is feasible.
“demonstrating the feasibility of annotation-free anomaly detection”
From A Comprehensive Survey of Transformer-Based Models for Video Anomaly Detection in Surveillance Systems (2025) · cites this paper as [21]
The performance of the Transformer Encoder-Decoder framework is constrained by reconstruction noise in dynamic traffic environments.
“though performance is constrained by reconstruction noise in dynamic traffic environments [21].”
From A Comprehensive Survey of Transformer-Based Models for Video Anomaly Detection in Surveillance Systems (2025) · cites this paper as [21]
Anomalies are flagged by high reconstruction errors in the unsupervised transformer framework.
“Anomalies are flagged by high reconstruction errors.”
From A Comprehensive Survey of Transformer-Based Models for Video Anomaly Detection in Surveillance Systems (2025) · cites this paper as [21]
The unsupervised transformer framework achieved state-of-the-art results on Drone-Anomaly and UIT-ADrone datasets.
“It achieved state-of-the-art results on Drone-Anomaly and UIT-ADrone datasets, making it highly suitable for aerial surveillance.”
From A Comprehensive Survey of Transformer-Based Models for Video Anomaly Detection in Surveillance Systems (2025) · cites this paper as [21]
The unsupervised transformer framework predicts future frames using a transformer encoder-decoder trained solely on normal traffic videos.
“This unsupervised framework predicts future frames using a transformer encoder-decoder trained solely on normal traffic videos.”
From A Comprehensive Survey of Transformer-Based Models for Video Anomaly Detection in Surveillance Systems (2025) · cites this paper as [21]
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- Authors
- Tran, T. M., Bui, D. C., Nguyen, T. V., & Nguyen, K.
- Affiliations
- Vietnam National University Ho Chi Minh City (VN)
- University of Dayton (US)
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2024-09
- Volume
- 34
- Issue
- 9
- Pages
- 8292-8309
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W4392693771
- Cited by
- 63
- References
- 76
- Citations by year
- 2026: 28 · 2025: 33 · 2024: 2
- FWCI
- 9.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Circuits and Systems for Video Technology (publishedVersion)
- Funders
- Vietnam National University, Ho Chi Minh City (B2023-26-01)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- traffic anomaly detection, unsupervised anomaly detection, aerial video surveillance, drone video, spatio-temporal representation, transformer encoder-decoder, future frame prediction, reconstruction error, traffic monitoring
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