The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking
Derek Gloudemans (Vanderbilt University),* Daniel Work (Vanderbilt University), Yanbing Wang (Vanderbilt University), Gracie E Gumm (Vanderbilt University), William Barbour (Vanderbilt University)
The 34th British Machine Vision Conference
Abstract
This work presents a novel video dataset recorded from overlapping highway traffic cameras along an urban interstate, enabling multi-camera 3D object tracking in a traffic monitoring context. Data is released from 3 scenes containing video from at least 16 cameras, totaling 57 minutes in length. 877,000 3D bounding boxes and corresponding object tracklets are fully and accurately annotated for each camera field of view and are combined into a spatially and temporally continuous set of vehicle trajectories for each scene. Lastly, existing algorithms are combined to benchmark a number of 3D multi-camera tracking pipelines on the dataset, with results indicating that the dataset is challenging due to the difficulty of matching objects traveling at high speeds across cameras and heavy object occlusion, potentially for hundreds of frames, during congested traffic. This work aims to enable the development of accurate and automatic vehicle trajectory extraction algorithms, which will play a vital role in understanding impacts of autonomous vehicle technologies on the safety and efficiency of traffic.Video
Citation
@inproceedings{Gloudemans_2023_BMVC,
author = {Derek Gloudemans and Daniel Work and Yanbing Wang and Gracie E Gumm and William Barbour},
title = {The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0015.pdf}
}
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