Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2019 IEEE Intelligent Vehicles Symposium, IV 2019 |
| Untertitel | Proceedings |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 1707-1714 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 978-1-7281-0560-4 |
| ISBN (Print) | 978-1-7281-0561-1 |
| Publikationsstatus | Veröffentlicht - Juni 2019 |
| Veranstaltung | 30th IEEE Intelligent Vehicles Symposium, IV 2019 - Paris, Frankreich Dauer: 9 Juni 2019 → 12 Juni 2019 |
Publikationsreihe
| Name | IEEE Intelligent Vehicles Symposium, Proceedings |
|---|---|
| Band | 2019-June |
| ISSN (Print) | 1931-0587 |
| ISSN (elektronisch) | 2642-7214 |
Abstract
In shared space, pedestrians are often found walking in groups and behaving differently than individual pedestrians. However, automatically detecting pedestrian groups with high accuracy is not trivial given the dynamic environment and interactions in mixed traffic. Instead of tedious manual work and in order to cope with large scales of data, we propose a time-sequence Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for pedestrian group detection. It is based on coexisting time and Euclidean distance between pedestrians. Our approach outputs reliable results with high IoU values. It can be easily adapted to other groups, e.g., cyclists and animals. In addition to individual behavior, the output data with differentiation of group behavior can be used in further studies in intent detection and motion prediction.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Ingenieurwesen (insg.)
- Fahrzeugbau
- Mathematik (insg.)
- Modellierung und Simulation
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2019 IEEE Intelligent Vehicles Symposium, IV 2019: Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. S. 1707-1714 8813849 (IEEE Intelligent Vehicles Symposium, Proceedings; Band 2019-June).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Pedestrian group detection in shared space
AU - Cheng, Hao
AU - Li, Yao
AU - Sester, Monika
N1 - Funding information: The authors cordially thank the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 227198829/GRK1931.
PY - 2019/6
Y1 - 2019/6
N2 - In shared space, pedestrians are often found walking in groups and behaving differently than individual pedestrians. However, automatically detecting pedestrian groups with high accuracy is not trivial given the dynamic environment and interactions in mixed traffic. Instead of tedious manual work and in order to cope with large scales of data, we propose a time-sequence Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for pedestrian group detection. It is based on coexisting time and Euclidean distance between pedestrians. Our approach outputs reliable results with high IoU values. It can be easily adapted to other groups, e.g., cyclists and animals. In addition to individual behavior, the output data with differentiation of group behavior can be used in further studies in intent detection and motion prediction.
AB - In shared space, pedestrians are often found walking in groups and behaving differently than individual pedestrians. However, automatically detecting pedestrian groups with high accuracy is not trivial given the dynamic environment and interactions in mixed traffic. Instead of tedious manual work and in order to cope with large scales of data, we propose a time-sequence Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for pedestrian group detection. It is based on coexisting time and Euclidean distance between pedestrians. Our approach outputs reliable results with high IoU values. It can be easily adapted to other groups, e.g., cyclists and animals. In addition to individual behavior, the output data with differentiation of group behavior can be used in further studies in intent detection and motion prediction.
UR - http://www.scopus.com/inward/record.url?scp=85072268383&partnerID=8YFLogxK
U2 - 10.1109/ivs.2019.8813849
DO - 10.1109/ivs.2019.8813849
M3 - Conference contribution
AN - SCOPUS:85072268383
SN - 978-1-7281-0561-1
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 1707
EP - 1714
BT - 2019 IEEE Intelligent Vehicles Symposium, IV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE Intelligent Vehicles Symposium, IV 2019
Y2 - 9 June 2019 through 12 June 2019
ER -