تقرير
Probabilistic Image-Driven Traffic Modeling via Remote Sensing
| العنوان: | Probabilistic Image-Driven Traffic Modeling via Remote Sensing |
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| المؤلفون: | Workman, Scott, Hadzic, Armin |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | This work addresses the task of modeling spatiotemporal traffic patterns directly from overhead imagery, which we refer to as image-driven traffic modeling. We extend this line of work and introduce a multi-modal, multi-task transformer-based segmentation architecture that can be used to create dense city-scale traffic models. Our approach includes a geo-temporal positional encoding module for integrating geo-temporal context and a probabilistic objective function for estimating traffic speeds that naturally models temporal variations. We evaluate our method extensively using the Dynamic Traffic Speeds (DTS) benchmark dataset and significantly improve the state-of-the-art. Finally, we introduce the DTS++ dataset to support mobility-related location adaptation experiments. Comment: European Conference on Computer Vision (ECCV) 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.05521 |
| رقم الأكسشن: | edsarx.2403.05521 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |