TY - GEN
T1 - Synthehicle
T2 - 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023
AU - Herzog, Fabian
AU - Chen, Junpeng
AU - Teepe, Torben
AU - Gilg, Johannes
AU - Hormann, Stefan
AU - Rigoll, Gerhard
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Smart City applications such as intelligent traffic routing, accident prevention or vehicle surveillance rely on computer vision methods for exact vehicle localization and tracking. Privacy issues make collecting real data difficult, and labeling data is a time-consuming and costly process. Due to the scarcity of accurately labeled data, detecting and tracking vehicles in 3D from multiple cameras proves challenging to explore. We present a massive synthetic dataset for multiple vehicle tracking and segmentation in multiple overlapping and non-overlapping camera views. Unlike existing datasets, which only provide tracking ground truth for 2D bounding boxes, our dataset additionally contains perfect labels for 3D bounding boxes in camera- and world coordinates, depth estimation, and instance, semantic and panoptic segmentation. The dataset consists of 17 hours of labeled video material, recorded from 340 cameras in 64 diverse day, rain, dawn, and night scenes, making it the most extensive dataset for multi-target multi-camera tracking so far. We provide baselines for detection, vehicle re-identification, and single- and multi-camera tracking. Code and data are publicly available. 11Code and data: https://github.com/fubel/synthehicle
AB - Smart City applications such as intelligent traffic routing, accident prevention or vehicle surveillance rely on computer vision methods for exact vehicle localization and tracking. Privacy issues make collecting real data difficult, and labeling data is a time-consuming and costly process. Due to the scarcity of accurately labeled data, detecting and tracking vehicles in 3D from multiple cameras proves challenging to explore. We present a massive synthetic dataset for multiple vehicle tracking and segmentation in multiple overlapping and non-overlapping camera views. Unlike existing datasets, which only provide tracking ground truth for 2D bounding boxes, our dataset additionally contains perfect labels for 3D bounding boxes in camera- and world coordinates, depth estimation, and instance, semantic and panoptic segmentation. The dataset consists of 17 hours of labeled video material, recorded from 340 cameras in 64 diverse day, rain, dawn, and night scenes, making it the most extensive dataset for multi-target multi-camera tracking so far. We provide baselines for detection, vehicle re-identification, and single- and multi-camera tracking. Code and data are publicly available. 11Code and data: https://github.com/fubel/synthehicle
UR - http://www.scopus.com/inward/record.url?scp=85148330190&partnerID=8YFLogxK
U2 - 10.1109/WACVW58289.2023.00005
DO - 10.1109/WACVW58289.2023.00005
M3 - Conference contribution
AN - SCOPUS:85148330190
T3 - Proceedings - 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023
SP - 1
EP - 11
BT - Proceedings - 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 January 2023 through 7 January 2023
ER -
