用交通摄像头视频高效估算车速，速度准且实时快。
Efficient Vision-based Vehicle Speed Estimation
- 基于2D检测与消失点几何，优化算法提升实时性。
- 误差仅0.58 km/h，检测精度达91.02%，比之前快5.5倍。
- 适合部署在边缘设备，兼顾速度与计算成本。
本文提出一种基于交通摄像头视频的高效车辆速度估计算法。在已有利用2D检测结果与消失点几何构建3D边界框方法的基础上，引入多项改进以提升实时性能。我们在BrnoCompSpeed数据集上评估了多种变体，在不同硬件平台（包括边缘设备）上的实验表明，相比先前最优方法，本方法在保持相当或更优的速度估计精度的同时，帧率显著提升。我们分析了准确率与计算成本的权衡关系，发现采用后训练量化的小型模型在真实场景部署中表现最佳。最优模型在中位数速度估计误差（0.58 km/h）、检测精确率（91.02%）和召回率（91.14%）方面均优于前序工作（分别为0.60 km/h、87.08%、83.32%），且推理速度提升5.5倍。
原文摘要 · Abstract (English)
This paper presents a computationally efficient method for vehicle speed estimation from traffic camera footage. Building upon previous work that utilizes 3D bounding boxes derived from 2D detections and vanishing point geometry, we introduce several improvements to enhance real-time performance. We evaluate our method in several variants on the BrnoCompSpeed dataset in terms of vehicle detection and speed estimation accuracy. Our extensive evaluation across various hardware platforms, including edge devices, demonstrates significant gains in frames per second (FPS) compared to the prior state-of-the-art, while maintaining comparable or improved speed estimation accuracy. We analyze the trade-off between accuracy and computational cost, showing that smaller models utilizing post-training quantization offer the best balance for real-world deployment. Our best performing model beats previous state-of-the-art in terms of median vehicle speed estimation error (0.58 km/h vs. 0.60 km/h), detection precision (91.02% vs 87.08%) and recall (91.14% vs. 83.32%) while also being 5.5 times faster.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。