So you think you can track?
- Vanderbilt University,Nashville,TN,37212
- Rutgers University-Camden,Camden,NJ,08102
- UC Berkeley University,Berkeley,CA,94720
- Temple University,Philadelphia,PA,19122
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. Video is recorded in cooperation with Tennessee State Department of Transportation and its policies. 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. Video data, scene information, and vehicle trajectories are made publicly available at i24motion.org.
- Research Organization:
- UC Berkeley; Vanderbilt University; Rutgers University-Camden; Temple University
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO)
- DOE Contract Number:
- EE0008872
- OSTI ID:
- 2545924
- Journal Information:
- 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Journal Name: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
- Country of Publication:
- United States
- Language:
- English
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