Details
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin) |
| Subtitle of host publication | Proceedings |
| Editors | Gordan Velikic, Christian Gross |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 353-356 |
| Number of pages | 4 |
| ISBN (electronic) | 9781728127453 |
| ISBN (print) | 9781728127750 |
| Publication status | Published - 8 Sept 2019 |
| Event | 9th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2019 - Berlin, Germany Duration: 8 Sept 2019 → 11 Sept 2019 |
Abstract
Keywords
- Advanced driver assistance, Neural networks, Pedestrian detection, Synthetic dataset
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
- Engineering(all)
- Media Technology
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2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin): Proceedings. ed. / Gordan Velikic; Christian Gross. Institute of Electrical and Electronics Engineers Inc., 2019. p. 353-356.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Deep Learning Based Classification of Pedestrian Vulnerability Trained on Synthetic Datasets
AU - Schleusner, Jens
AU - Neu, Lothar
AU - Behmann, Nicolai
AU - Blume, Holger
PY - 2019/9/8
Y1 - 2019/9/8
N2 - The reliable detection of vulnerable road users and the assessment of the actual vulnerability is an important task for the collision warning algorithms of driver assistance systems. Current systems make assumptions about the road geometry which can lead to misclassification. We propose a deep learning-based approach to reliably detect pedestrians and classify their vulnerability based on the traffic area they are walking in. Since there are no pre-labeled datasets available for this task, we developed a method to train a network first on custom synthetic data and then use the network to augment a customer-provided training dataset for a neural network working on real world images. The evaluation shows that our network is able to accurately classify the vulnerability of pedestrians in complex real world scenarios without making assumptions on road geometry.
AB - The reliable detection of vulnerable road users and the assessment of the actual vulnerability is an important task for the collision warning algorithms of driver assistance systems. Current systems make assumptions about the road geometry which can lead to misclassification. We propose a deep learning-based approach to reliably detect pedestrians and classify their vulnerability based on the traffic area they are walking in. Since there are no pre-labeled datasets available for this task, we developed a method to train a network first on custom synthetic data and then use the network to augment a customer-provided training dataset for a neural network working on real world images. The evaluation shows that our network is able to accurately classify the vulnerability of pedestrians in complex real world scenarios without making assumptions on road geometry.
KW - Advanced driver assistance
KW - Neural networks
KW - Pedestrian detection
KW - Synthetic dataset
U2 - 10.15488/16562
DO - 10.15488/16562
M3 - Conference contribution
AN - SCOPUS:85078929180
SN - 9781728127750
SP - 353
EP - 356
BT - 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin)
A2 - Velikic, Gordan
A2 - Gross, Christian
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2019
Y2 - 8 September 2019 through 11 September 2019
ER -