In this paper we propose an automatic trajectory data reconciliation to correct common errors in vision-based vehicle trajectory data. Given "raw" vehicle detection and tracking information from automatic video processing algorithms, we propose a pipeline including (a) an online data association algorithm to match fragments that describe the same object (vehicle), which is formulated as a min-cost network circulation problem of a graph, and (b) a one-step trajectory rectification procedure formulated as a quadratic program to enhance raw detection data. The pipeline leverages vehicle dynamics and physical constraints to associate tracked objects when they become fragmented, remove measurement noises and outliers and impute missing data due to fragmentations. We assess the capability of the proposed two-step pipeline to reconstruct three benchmarking datasets: (1) a microsimulation dataset that is artificially downgraded to replicate upstream errors, (2) a 15-min NGSIM data that is manually perturbed, and (3) tracking data consists of 3 scenes from collections of video data recorded from 16-17 cameras on a section of the I-24 MOTION system, and compare with the corresponding manually-labeled ground truth vehicle bounding boxes. All of the experiments show that the reconciled trajectories improve the accuracy on all the tested input data for a wide range of measures. Lastly, we show the design of a software architecture that is currently deployed on the full-scale I-24 MOTION system consisting of 276 cameras that covers 4.2 miles of I-24. We demonstrate the scalability of the proposed reconciliation pipeline to process high-volume data on a daily basis.

翻译：本文提出一种自动轨迹数据协调方法，用以纠正基于视觉的车辆轨迹数据中常见误差。针对自动视频处理算法生成的原始车辆检测与跟踪信息，我们设计了一套处理流程，包含：（a）一种在线数据关联算法，用于匹配描述同一对象（车辆）的碎片化片段，该问题被建模为图的最小费用网络流问题；（b）一种基于二次规划的轨迹单步修正过程，用以增强原始检测数据。该流程利用车辆动力学与物理约束，在跟踪对象出现碎片化时进行关联，消除测量噪声与异常值，并补全因碎片化导致的数据缺失。我们评估了所提出的两阶段流程在三个基准数据集上的重建能力：（1）一个经人为降质以复现上游误差的微观仿真数据集；（2）人工添加扰动的15分钟NGSIM数据；（3）来自I-24 MOTION系统中16至17台摄像机在4.2英里路段采集的视频数据（包含3个场景），并与对应的人工标注真实车辆边界框进行对比。所有实验表明，协调后的轨迹在多种评价指标上均提升了所有测试输入数据的精度。最后，我们展示了已部署于全规模I-24 MOTION系统（含276台摄像机、覆盖I-24公路4.2英里路段）的软件架构设计，证实了所提协调流程每日处理海量数据的可扩展性。