Name
Title | ||
|---|---|---|
Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-oriented Synthetic Data Generation in Crowded Scenes |
Abstract | ||
|---|---|---|
The evaluation of camera-based perception functions in automated driving (AD) is a significant challenge and requires large-scale high-quality datasets. Recently proposed metrics for safety evaluation additionally require detailed per-instance annotations of dynamic properties such as distance and velocities that may not be available in openly accessible AD datasets. Synthetic data from 3D simulators like CARLA may provide a solution to this problem as labeled data can be produced in a structured manner. However, CARLA currently lacks instance segmentation ground truth. In this paper, we present a back projection pipeline that allows us to obtain accurate instance segmentation maps for CARLA, which is necessary for precise per-instance ground truth information. Our evaluation results show that per-pedestrian depth aggregation obtained from our instance segmentation is more precise than previously available approximations based on bounding boxes especially in the context of crowded scenes in urban automated driving. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICCVW54120.2021.00115 | 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021) |
DocType | Volume | Issue |
Conference | 2021 | 1 |
ISSN | Citations | PageRank |
2473-9936 | 0 | 0.34 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Lyssenko | 1 | 1 | 1.09 |
| Christoph Gladisch | 2 | 6 | 1.86 |
| Christian Heinzemann | 3 | 137 | 15.50 |
| Matthias Woehrle | 4 | 194 | 21.93 |
| Rudolph Triebel | 5 | 711 | 58.20 |