tue-mps / simple-tad

Simplifying Traffic Anomaly Detection with Video Foundation Models

Svetlana Orlova, Tommie Kerssies, Brun´o B. Englert, Gijs Dubbelman
Eindhoven University of Technology

Recent methods for ego-centric Traffic Anomaly Detection (TAD) often rely on complex multi-stage or multi-representation fusion architectures, yet it remains unclear whether such complexity is necessary. Recent findings in visual perception suggest that foundation models, enabled by advanced pre-training, allow simple yet flexible architectures to outperform specialized designs. Therefore, in this work, we investigate an architecturally simple encoder-only approach using plain Video Vision Transformers (Video ViTs) and study how pre-training enables strong TAD performance. We find that: (i) advanced pre-training enables simple encoder-only models to match or even surpass the performance of specialized state-of-the-art TAD methods, while also being significantly more efficient; (ii) although weakly- and fully-supervised pre-training are advantageous on standard benchmarks, we find them less effective for TAD. Instead, self-supervised Masked Video Modeling (MVM) provides the strongest signal; and (iii) Domain-Adaptive Pre-Training (DAPT) on unlabeled driving videos further improves downstream performance, without requiring anomalous examples. Our findings highlight the importance of pre-training and show that effective, efficient, and scalable TAD models can be built with minimal architectural complexity.

✨ DoTA and DADA-2000 results

Video ViT-based encoder-only models set a new state of the art on both datasets, while being significantly more efficient than top-performing specialized methods. FPS measured using NVIDIA A100 MIG, 2 1 GPU. † From prior work. ‡ Optimistic estimates using publicly available components of the model. “A→B”: trained on A, tested on B; D2K: DADA-2000.

🧩 Code

Check out our GitHub repo: simple-tad

📍Model Zoo

DAPT (adapted) models

Fine-tuned on DoTA

Fine-tuned on DADA-2000

☎️ Contact

Svetlana Orlova: [email protected] , [email protected]

👍 Acknowledgements

Our code is mainly based on the VideoMAE codebase. With Video ViTs that have identical architecture, we only used their weights: ViViT , VideoMAE2 , SMILE , SIGMA , MME , MGMAE .
We used fragments of original implementations of MVD , InternVideo2 , and UMT to integrate these models with our codebase.

✏️ Citation

If you think this project is helpful, please feel free to like us ❤️ and cite our paper:

@inproceedings{orlova2025simplifying,
  title={Simplifying Traffic Anomaly Detection with Video Foundation Models},
  author={Orlova, Svetlana and Kerssies, Tommie and Englert, Brun{\'o} B and Dubbelman, Gijs},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2025}
}

@article{orlova2025simplifying,
  title={Simplifying Traffic Anomaly Detection with Video Foundation Models},
  author={Orlova, Svetlana and Kerssies, Tommie and Englert, Brun{\'o} B and Dubbelman, Gijs},
  journal={arXiv preprint arXiv:2507.09338},
  year={2025}
}