Dual Encoder-Decoder-Encoder with Adversarial Training for Unsupervised Traffic Accident Detection in Surveillance Videos.

To enhance road safety and improve emergency response, traffic incidents should be detected in real-world surveillance footage as quickly as possible. Existing systems largely depend on manual monitoring, which is time-consuming and prone to error. Automated accident detection remains challenging due to the substantial class imbalance: normal driving situations are overrepresented, whereas accidents are rare and diverse. In such cases, traditional computer vision systems often cannot reliably differentiate between normal and abnormal events. This study addresses the problem by developing a deep learning architecture based on a dual encoder-decoder-encoder (EDE) framework. The model uses two shared encoder-decoder pipelines to map image distributions to specified latent distributions in both directions. This framework enables the system to model common traffic behavior patterns and become more sensitive to changes that may indicate dangerous or unusual events. A two-phase training technique is proposed to further improve anomaly detection. In the first phase, the model learns to reconstruct images of normal driving, using reconstruction loss to characterize normal behavior. In the second phase, a generative adversarial mechanism is introduced: reconstructed latent vectors from one EDE are passed to the other, generating synthetic images and latent spaces. This process amplifies differences between real and synthetic outputs, making the system more responsive to subtle signs of potential anomalies. The dual-EDE architecture and adversarial training methodology represent a substantial advance over current methods by modeling both normal and pathological behavior. Experimental results on real-world traffic surveillance datasets demonstrate that the proposed method significantly improves the detection of accidents and unsafe driving behaviors, both in terms of accuracy and robustness.