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 fragments that describe the same object (vehicle), which is formulated as a min-cost network circulation problem of a graph, and 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 the errors from prior image processing step, (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.

中文翻译：

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大规模自动车辆轨迹数据重建

在本文中，我们提出了一种自动轨迹数据协调方法，以纠正基于视觉的车辆轨迹数据中的常见错误。给定来自自动视频处理算法的“原始”车辆检测和跟踪信息，我们提出了一个包含描述同一对象（车辆）的片段的管道，该管道被公式化为图的最小成本网络循环问题，并增强原始检测数据。该管道利用车辆动力学和物理约束在跟踪对象变得碎片时将其关联起来，消除测量噪声和异常值，并估算由于碎片而丢失的数据。我们评估了所提出的两步管道重建三个基准数据集的能力：（1）人为降级以复制先前图像处理步骤中的错误的微观模拟数据集，（2）手动扰动的 15 分钟 NGSIM 数据，(3) 跟踪数据由 I-24 MOTION 系统一部分上 16-17 个摄像机记录的视频数据集合中的 3 个场景组成，并与相应的手动标记的地面实况车辆边界框进行比较。所有实验都表明，协调后的轨迹提高了各种测量的所有测试输入数据的准确性。最后，我们展示了目前部署在全尺寸 I-24 MOTION 系统上的软件架构设计，该系统由 276 个摄像头组成，覆盖 4.2 英里的 I-24。我们展示了所提出的协调管道的可扩展性，可以每天处理大量数据。