[Submitted on 22 Oct 2026]
Title:StateBridge: Phase- and Track-Grounded Reasoning for Synthetic-to-Real Traffic Video Captioning and VQA
View a PDF of the paper titled StateBridge: Phase- and Track-Grounded Reasoning for Synthetic-to-Real Traffic Video Captioning and VQA, by Blessing Agyei Kyem and Richard Dzinyela and Joshua Kofi Asamoah and Eugene Denteh and Andrews Danyo and Solomon Owusu-Ansah and Dickson Gyimah Asiedu and Armstrong Aboah
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Abstract:Traffic safety videos provide rich evidence for understanding interactions between pedestrians and vehicles, but synthetic-to-real evaluation makes this setting challenging because models must learn from simulated scenes and generalize to real-world traffic. In this work, we present StateBridge, a traffic video-language framework designed to operate under this synthetic-only training protocol. Our approach uses phase-aware clip construction, pedestrian and vehicle track summaries, and structured prompts to ground vision-language models in stable traffic evidence rather than appearance alone. We fine-tune public vision-language models on synthetic captioning and visual question answering examples, then combine their outputs through fact-locked caption fusion and evidence-diverse VQA consensus to improve consistency across actor roles, behavioral phases, and question types. StateBridge achieves an official S2 score of 55.4679 on AI City Challenge 2026 Track 2 using only synthetic training data. These results demonstrate the effectiveness of structured evidence transfer for traffic safety captioning and visual question answering.
Comments: 25 pages, 5 figures, ECCV conference
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.31898 [cs.CV]
(or arXiv:2610.31898v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.31898
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Blessing Agyei Kyem [view email] [v1] Thu, 22 Oct 2026 20:52:22 UTC (1,030 KB)
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