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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.12723 |
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| _version_ | 1866909499873296384 |
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| author | Kumar, Bhaiya Vaibhaw Rawat, Deepti Kandalla, Tanvi Nagariya, Aarnav Vemuri, Kavita |
| author_facet | Kumar, Bhaiya Vaibhaw Rawat, Deepti Kandalla, Tanvi Nagariya, Aarnav Vemuri, Kavita |
| contents | This paper presents the myEye2Wheeler dataset, a unique resource of real-world gaze behaviour of two-wheeler drivers navigating complex Indian traffic. Most datasets are from four-wheeler drivers on well-planned roads and homogeneous traffic. Our dataset offers a critical lens into the unique visual attention patterns and insights into the decision-making of Indian two-wheeler drivers. The analysis demonstrates that existing saliency models, like TASED-Net, perform less effectively on the myEye-2Wheeler dataset compared to when applied on the European 4-wheeler eye tracking datasets (DR(Eye)VE), highlighting the need for models specifically tailored to the traffic conditions. By introducing the dataset, we not only fill a significant gap in two-wheeler driver behaviour research in India but also emphasise the critical need for developing context-specific saliency models. The larger aim is to improve road safety for two-wheeler users and lane-planning to support a cost-effective mode of transport. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12723 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | myEye2Wheeler: A Two-Wheeler Indian Driver Real-World Eye-Tracking Dataset Kumar, Bhaiya Vaibhaw Rawat, Deepti Kandalla, Tanvi Nagariya, Aarnav Vemuri, Kavita Computer Vision and Pattern Recognition This paper presents the myEye2Wheeler dataset, a unique resource of real-world gaze behaviour of two-wheeler drivers navigating complex Indian traffic. Most datasets are from four-wheeler drivers on well-planned roads and homogeneous traffic. Our dataset offers a critical lens into the unique visual attention patterns and insights into the decision-making of Indian two-wheeler drivers. The analysis demonstrates that existing saliency models, like TASED-Net, perform less effectively on the myEye-2Wheeler dataset compared to when applied on the European 4-wheeler eye tracking datasets (DR(Eye)VE), highlighting the need for models specifically tailored to the traffic conditions. By introducing the dataset, we not only fill a significant gap in two-wheeler driver behaviour research in India but also emphasise the critical need for developing context-specific saliency models. The larger aim is to improve road safety for two-wheeler users and lane-planning to support a cost-effective mode of transport. |
| title | myEye2Wheeler: A Two-Wheeler Indian Driver Real-World Eye-Tracking Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.12723 |