A New Method for Validating and Generating Vehicle Trajectories From Stationary Video Cameras
A New Method for Validating and Generating Vehicle Trajectories From Stationary Video Cameras
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DOI：
10.1109/tits.2022.3149277
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发表时间：
2022-09
影响因子：
8.5
通讯作者：
B. Coifman;Lizhe Li
中科院分区：
文献类型：
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作者：
B. Coifman;Lizhe Li
基于图像处理的车辆跟踪是交通监控的有力工具，但容易出错。阻碍测量时间序列速度和加速度的相对小的误差可能难以检测，例如，在100米长的弹道上有1米的定位误差。本文提出了一种有效的方法来分离的定位误差从车辆行驶的评估基于图像处理的车辆轨迹。该方法从时空切片STS开始，STS实际上是从视频中采样的视觉时空图。这项工作使STS倾斜，使给定的轨迹变平，消除轨迹中记录的车辆行驶。相对于行进的距离而言难以察觉的定位误差在平坦的轨道中变得显而易见。因此，提供了一种快速评估来自几乎任何图像处理系统的报告轨迹与原始视频数据中的真实车辆位置的手段。认识到展平过程是双向的，如果给定轨迹中的错误很明显，STS方法也可以用来快速修复它们。从而在整个给定轨迹中提供精确的瞬时速度和加速度的路径。或者，可以使用该过程直接从STS生成车辆轨迹。虽然主要重点是纵向跟踪，但该过程也可用于评估（提取）给定车辆的横向位置。使用NGSIM，Cityflow和UA-DETRAC数据集对该方法进行评估，在每种情况下，都显示了这项工作如何提高给定数据集的保真度。
Image processing based vehicle tracking is a powerful tool for monitoring traffic, but it is error prone. Relatively small errors that impede measuring time-series speed and acceleration can be hard to detect, e.g., 1 m positioning error in a 100 m long trajectory. This paper presents an efficient approach to separate the positioning errors from vehicle travel for evaluating image processing based vehicle trajectories. The approach starts with a spatiotemporal slice, STS, which is effectively a visual time-space diagram sampled from the video. This work skews the STS to flatten a given trajectory, eliminating the vehicle travel recorded in the trajectory. Positioning errors that were imperceptible relative to the distance traveled become readily apparent in the flattened track. Thus, providing a means to quickly assess reported trajectories from almost any image processing system against the true vehicle positions in the original video data. Recognizing that the flattening process works both ways, if errors are evident in a given trajectory, the STS method can also be used to quickly fix them. Thereby providing a path to accurate instantaneous speed and acceleration throughout the given trajectory. Alternatively, one can use this process to generate vehicle trajectories directly from the STS. While the main focus is longitudinal tracking, the process can also be used to assess (extract) the lateral position of a given vehicle. The method is evaluated using the NGSIM, Cityflow and UA-DETRAC datasets, in each case it is shown how this work can increase the fidelity of the given dataset.