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arXiv · 2504.03235

Enhancing Traffic Incident Response through Sub-Second Temporal Localization with HybridMamba

Ibne Farabi Shihab·Sanjeda Akter·Anuj Sharma
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Abstract

Traffic crash detection in long-form surveillance videos is essential for improving emergency response and infrastructure planning, yet remains difficult due to the brief and infrequent nature of crash events. We present \textbf{HybridMamba}, a novel architecture integrating visual transformers with state-space temporal modeling to achieve high-precision crash time localization. Our approach introduces multi-level token compression and hierarchical temporal processing to maintain computational efficiency without sacrificing temporal resolution. Evaluated on a large-scale dataset from the Iowa Department of Transportation, HybridMamba achieves a mean absolute error of \textbf{1.50 seconds} for 2-minute videos ($p<0.01$ compared to baselines), with \textbf{65.2%} of predictions falling within one second of the ground truth. It outperforms recent video-language models (e.g., TimeChat, VideoLLaMA-2) by up to 3.95 seconds while using significantly fewer parameters (3B vs. 13--72B). Our results demonstrate effective temporal localization across various video durations (2--40 minutes) and diverse environmental conditions, highlighting HybridMamba's potential for fine-grained temporal localization in traffic surveillance while identifying challenges that remain for extended deployment.

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Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma. 2025-04-04. Enhancing Traffic Incident Response through Sub-Second Temporal Localization with HybridMamba. https://arxiv.org/abs/2504.03235

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