Real-Time Vehicle Tracking, Counting, Classification, and Obstacle Detection on Lane
Fuente:
Zenodo
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Recurso digital |
| Langue: | anglais |
| Publié: |
Zenodo
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866901391768813568 |
|---|---|
| author | Kumaravel, Hari Raam Kandasamy, Janaki |
| author_facet | Kumaravel, Hari Raam Kandasamy, Janaki |
| contents | <p>This paper presents a comparative study of two deep learning-based object detection approaches: a Vision Transformer (ViT)-based detector and a fine-tuned YOLOv8 nano model. The ViT model is trained on the Pascal VOC 2012 dataset, while YOLOv8 is trained on a domain-specific vehicle dataset obtained from Roboflow.</p> <p>The proposed system performs real-time vehicle tracking, classification, and traffic violation detection, including stop-line violations, restricted zone intrusion, and wrong-way driving. A complete end-to-end pipeline is developed covering data preprocessing, model training, inference, and visualization.</p> <p>Experimental results demonstrate that YOLOv8 achieves efficient real-time inference suitable for deployment, while the Vision Transformer provides richer global feature representations at the cost of higher computational complexity. The study highlights key trade-offs between transformer-based and convolution-based detection architectures for real-world intelligent traffic monitoring systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19710577 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Real-Time Vehicle Tracking, Counting, Classification, and Obstacle Detection on Lane Kumaravel, Hari Raam Kandasamy, Janaki Object Detection Vision Transformer Vehicle Tracking Traffic Monitoring Real-Time Detection <p>This paper presents a comparative study of two deep learning-based object detection approaches: a Vision Transformer (ViT)-based detector and a fine-tuned YOLOv8 nano model. The ViT model is trained on the Pascal VOC 2012 dataset, while YOLOv8 is trained on a domain-specific vehicle dataset obtained from Roboflow.</p> <p>The proposed system performs real-time vehicle tracking, classification, and traffic violation detection, including stop-line violations, restricted zone intrusion, and wrong-way driving. A complete end-to-end pipeline is developed covering data preprocessing, model training, inference, and visualization.</p> <p>Experimental results demonstrate that YOLOv8 achieves efficient real-time inference suitable for deployment, while the Vision Transformer provides richer global feature representations at the cost of higher computational complexity. The study highlights key trade-offs between transformer-based and convolution-based detection architectures for real-world intelligent traffic monitoring systems.</p> |
| title | Real-Time Vehicle Tracking, Counting, Classification, and Obstacle Detection on Lane |
| topic | Object Detection Vision Transformer Vehicle Tracking Traffic Monitoring Real-Time Detection |
| url | https://doi.org/10.5281/zenodo.19710577 |