  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.
