发表机构
Universidade Federal de Alagoas（阿拉戈斯联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种面向嵌入式设备的类别无关几何车辆计数方法，无需物体模型与训练集，在树莓派硬件上快于实时运行，实地部署准确率达91%，适用于无标注数据等场景。
AI 中文摘要
当前交通数据采集主要依赖深度目标检测器结合检测后跟踪的流程，该流程的前提是实际中往往缺失的条件：已针对要计数的类别训练好检测器。本文重新审视了一种面向单板计算机的纯几何交通感知流程，其中检测是类别无关的：运动物体来自背景减法和阈值处理，计数由道路上假想线（即软件感应线圈检测器）的几何规则决定。该方法无需物体模型、训练集或单个物体轨迹，在树莓派（Raspberry Pi）类硬件上运行速度快于实时。本文描述了两种计数规则：恒定平均速度规则，在高斯速度分布下其预期准确率经分析推导约为86%；以及自校准预校准规则，该规则可从 blob 统计数据恢复车道几何形状并计数车道占用边缘，还能额外无额外成本地输出每辆车的平均速度。在四个视频上，后者的计数准确率为83.3%-100%；在实地部署中，在相同计算预算下，其准确率达91%，而 blob 跟踪基线方法仅为37.5%。本文详细报告了该阶段的观察结果：准确率下降的分辨率下限、车辆在计数线处混叠的帧率下限、短精选片段与长无控制 footage 之间的差距，以及 Python（更易调参，100% CPU）与 C++（40% CPU，热稳定性好）之间的权衡。这些是采样几何的属性，而非当时硬件的属性，至今仍制约着边缘部署。最后，本文指出基于运动的类别无关检测仍适用的场景：无标注数据的开放集类别、严格功耗预算、隐私受限的安装，以及挖掘训练样本以启动学习检测器的冷启动阶段。
英文摘要
Traffic data collection is dominated today by deep object detectors followed by tracking-by-detection, a pipeline that presupposes what is often missing in practice: a detector already trained on the class one wants to count. We revisit a purely geometric traffic-sensing pipeline for Single Board Computers in which detection is class-agnostic: moving objects come from background subtraction and thresholding, and counting is decided by a geometric rule on an imaginary line across the road, a software inductive loop detector. With no object model, training set or per-object trajectory, it runs faster than real time on Raspberry Pi class hardware. Two counting rules are described: a constant average speed rule, whose expected accuracy is derived analytically as about 86% under a Gaussian speed distribution, and a self-calibrating pre-calibration rule that recovers the lane geometry from blob statistics and counts edges of lane occupancy, additionally yielding per-vehicle average speed at no extra cost. Over four videos the latter counts with 83.3%-100% accuracy; in a field deployment it reaches 91% against 37.5% for a blob-tracking baseline under the same compute budget. We report the observations of that period in detail: the resolution floor below which accuracy collapses, the frame rate floor at which vehicles alias past the counting line, the gap between short curated clips and long uncontrolled footage, and the trade-off between Python (easier to tune, 100% CPU) and C++ (40% CPU, thermally viable). These are properties of the sampling geometry, not of the hardware of the time, and still constrain edge deployments. We close by arguing where motion-based, class-agnostic detection remains the right tool: open-set classes with no annotated data, tight power budgets, privacy-constrained installations, and the cold start of mining training crops to bootstrap a learned detector.
