7 research outputs found
Virtual trajectories for I-24 MOTION: data and tools
This article introduces a new virtual trajectory dataset derived from the
I-24 MOTION INCEPTION v1.0.0 dataset to address challenges in analyzing large
but noisy trajectory datasets. Building on the concept of virtual trajectories,
we provide a Python implementation to generate virtual trajectories from large
raw datasets that are typically challenging to process due to their size. We
demonstrate the practical utility of these trajectories in assessing speed
variability and travel times across different lanes within the INCEPTION
dataset. The virtual trajectory dataset opens future research on traffic waves
and their impact on energy
So you think you can track?
This work introduces a multi-camera tracking dataset consisting of 234 hours
of video data recorded concurrently from 234 overlapping HD cameras covering a
4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. The video
is recorded during a period of high traffic density with 500+ objects typically
visible within the scene and typical object longevities of 3-15 minutes. GPS
trajectories from 270 vehicle passes through the scene are manually corrected
in the video data to provide a set of ground-truth trajectories for
recall-oriented tracking metrics, and object detections are provided for each
camera in the scene (159 million total before cross-camera fusion). Initial
benchmarking of tracking-by-detection algorithms is performed against the GPS
trajectories, and a best HOTA of only 9.5% is obtained (best recall 75.9% at
IOU 0.1, 47.9 average IDs per ground truth object), indicating the benchmarked
trackers do not perform sufficiently well at the long temporal and spatial
durations required for traffic scene understanding