2025/04/04 by Ibne Farabi Shihab, Shihab, Ibne Farabi, Sanjeda Akter +3 · 3 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Cognitive Radio Networks and Spectrum Sensing #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #IoT and GPS-based Vehicle Safety Systems
paper · pdf · doi:10.48550/arxiv.2504.03235
openalex publication_date 2025/04/04 · openalex created_date 2025/10/12 · openalex updated_date 2026/07/28
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 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 1.50 seconds for 2-minute videos (p<0.01 compared to baselines), with 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.