So you think you can track?
Derek Gloudemans, Gergely Zach\'ar, Yanbing Wang, Junyi Ji, Matt Nice,, Matt Bunting, William Barbour, Jonathan Sprinkle, Benedetto Piccoli, Maria, Laura Delle Monache, Alexandre Bayen, Benjamin Seibold, Daniel B. Work

TL;DR
This paper presents a large-scale multi-camera vehicle tracking dataset with extensive annotations and benchmarks tracking algorithms, highlighting the challenges in long-duration traffic scene understanding.
Contribution
It introduces a comprehensive multi-camera traffic dataset with ground-truth trajectories and provides initial benchmarking results for tracking algorithms in complex highway scenes.
Findings
Tracking algorithms perform poorly with a best HOTA of 9.5%.
High recall (75.9%) achieved at low IOU (0.1).
Average of 47.9 IDs per ground truth object.
Abstract
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),…
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Videos
So You Think You Can Track?· youtube
Taxonomy
TopicsVideo Surveillance and Tracking Methods · Target Tracking and Data Fusion in Sensor Networks · Automated Road and Building Extraction
MethodsGreedy Policy Search
