A New Method for Validating and Generating Vehicle Trajectories From Stationary Video Cameras
Benjamin Coifman, Lizhe Li
Abstract
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.