用视频识别车轴，解决长车遮挡难题。
TRAX: TRacking Axles for Accurate Axle Count Estimation
- 结合YOLO-OBB和YOLO检测车辆与轮胎，智能关联匹配。
- 提出TRAX追踪算法，减少长车遮挡导致的漏检误检。
- 适合交通监控、智能收费等需要精准计轴的场景。
精准统计车轴数量对交通管理、收费及基础设施建设至关重要。本文提出一种端到端的视频驱动轴数估计方法，针对密集场景下现有技术的局限性进行改进。系统采用YOLO-OBB检测并分类车辆，同时使用YOLO检测轮胎，通过智能关联轮胎与所属车辆，实现复杂场景下的精准轴数预测。针对长车辆在遮挡和部分可见情况下的检测挑战，提出创新的TRAX（Tire and Axle Tracking）算法，有效追踪帧间轴相关特征。该方法显著降低误报率，提升长车轴数统计准确率，在真实交通视频中表现出强鲁棒性。本工作为可扩展的AI驱动轴数统计系统迈出关键一步，推动机器视觉替代传统路侧基础设施。
原文摘要 · Abstract (English)
Accurate counting of vehicle axles is essential for traffic control, toll collection, and infrastructure development. We present an end-to-end, video-based pipeline for axle counting that tackles limitations of previous works in dense environments. Our system leverages a combination of YOLO-OBB to detect and categorize vehicles, and YOLO to detect tires. Detected tires are intelligently associated to their respective parent vehicles, enabling accurate axle prediction even in complex scenarios. However, there are a few challenges in detection when it comes to scenarios with longer and occluded vehicles. We mitigate vehicular occlusions and partial detections for longer vehicles by proposing a novel TRAX (Tire and Axle Tracking) Algorithm to successfully track axle-related features between frames. Our method stands out by significantly reducing false positives and improving the accuracy of axle-counting for long vehicles, demonstrating strong robustness in real-world traffic videos. This work represents a significant step toward scalable, AI-driven axle counting systems, paving the way for machine vision to replace legacy roadside infrastructure.
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。