So You Think You Can Track?

Derek Gloudemans, Gergely Zachár, Yanbing Wang, Junyi Ji, Matt Nice, Matt Bunting, William W. Barbour, Jonathan Sprinkle, Benedetto Piccoli, Maria Laura Delle Monache, Alexandre Bayen, Benjamin Seibold, Daniel B. Work; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 4528-4538


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.

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@InProceedings{Gloudemans_2024_WACV, author = {Gloudemans, Derek and Zach\'ar, Gergely and Wang, Yanbing and Ji, Junyi and Nice, Matt and Bunting, Matt and Barbour, William W. and Sprinkle, Jonathan and Piccoli, Benedetto and Monache, Maria Laura Delle and Bayen, Alexandre and Seibold, Benjamin and Work, Daniel B.}, title = {So You Think You Can Track?}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2024}, pages = {4528-4538} }