A Review of Unsupervised Learning Based Anomaly Detection in Traffic Videos
Siddhant Buchade, Gopal Sakarkar · 2025
Anomaly detection in traffic surveillance videos is essential for enhancing road safety and traffic management. This review examines studies that employ unsupervised learning techniques for anomaly detection (AD) in traffic videos. The techniques include Autoencoders (AE), Generative Adversarial Networks (GAN) which are categorized as reconstruction-based methods and Clustering based methods. We categorize anomalies based on different characteristics, discuss benchmark datasets commonly used in unsupervised learning, and evaluate key performance metrics. Additionally, we explore real-world applications of unsupervised AD, highlight existing challenges, and outline potential future directions for advancing this field. This comprehensive review provides insights into the progress and limitations of unsupervised learning approaches for traffic anomaly detection.