用于相机无关转向运动计数的车辆轨迹进出区域无监督检测
Unsupervised Detection of Entry and Exit Regions from Vehicle Trajectories for Camera-Agnostic Turning Movement Counts
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中文总结 AI 辅助
研究针对相机无关转向运动计数，提出从车辆轨迹中无监督识别进出区域的流程，通过聚类初始和终点位置生成区域多边形分类轨迹，经多参数评估给出推荐配置，该流程稳定性高、计算成本低，校准片段等可提高区域估计质量。
中文摘要 AI 辅助
转向运动计数对交叉路口级交通管理至关重要，但由于每个相机区域标注成本高，其收集主要靠人工。本文提出一种无监督流程，直接从通过目标检测和多目标跟踪提取的原始车辆轨迹中识别进出区域，无需人工标注、相机校准或交叉路口几何先验知识。与轨迹聚类方法不同，该流程聚类初始和终点位置以生成持久空间区域多边形，按点在多边形内包含关系以线性成本对未来轨迹分类。该流程包括六个步骤，通过对印度班加罗尔25个监控摄像头捕获的密集异构交通以及UA - DETRAC基准数据集的10个序列进行19152次流程执行的系统统计分析来评估可配置参数。参数和非参数测试框架确定了三个一致显著的参数并给出经验性推荐配置。在此配置下，该流程在所有25个摄像头（包括16个预留位置）上实现了约3%的中位数分类误差以及符合工程阈值的GEH值。与两个轨迹聚类基线相比，该流程在不同相机视图下具有更高稳定性和更低计算成本，但中位数误差更高。扩展评估表明至少60分钟的校准片段和高峰流量选择可进一步提高区域估计质量。
英文摘要
Turning movement counts are essential for intersection-level traffic management, yet their collection remains predominantly manual due to the cost of per-camera region annotation. This paper presents an unsupervised pipeline that identifies entry and exit regions directly from raw vehicle trajectories extracted via object detection and multi-object tracking, requiring no manual annotation, camera calibration, or prior knowledge of intersection geometry. Unlike trajectory clustering methods that classify individual trajectories using pairwise similarity and must be re-executed on every new batch, the proposed pipeline clusters initial and terminal point locations to produce persistent spatial region polygons that classify future trajectories by point-in-polygon containment at linear cost. The pipeline comprises six sequential steps, five of which introduce configurable parameters evaluated through a systematic statistical analysis spanning 17,100 pipeline executions across 9 surveillance cameras capturing dense heterogeneous traffic in Bengaluru, India, and 10 sequences from the UA-DETRAC benchmark dataset. Both parametric and nonparametric testing frameworks identify three consistently significant parameters and yield an empirically grounded recommended configuration. Under this configuration, the pipeline achieves a median classification error of 3.4% across all 25 Bengaluru cameras, including 16 held-out locations, with a median per-turning-movement GEH of 2.43. Compared with two trajectory clustering baselines, the proposed pipeline exhibits greater stability across camera views and lower computational cost, at the expense of higher median error. Extended evaluation demonstrates that calibration clips of at least 60 minutes and peak-traffic selection further improve region estimation quality.
发表机构
- Robert Bosch Centre for Cyber-Physical Systems (RBCCPS), Centre for infrastructure, Sustainable Transportation and Urban Planning (CiSTUP)（罗伯特·博世网络物理系统中心（RBCCPS），基础设施、可持续交通与城市规划中心（CiSTUP））
- Indian Institute of Science (IISc)（印度科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
