TY - GEN
T1 - Ellipse detection for visual cyclists analysis “in the wild”
AU - Eldesokey, Abdelrahman
AU - Felsberg, Michael
AU - Khan, Fahad Shahbaz
N1 - Funding Information:
Acknowledgments. This work has been supported by VR (EMC2, ELLIIT, starting grant [2016-05543]) and Vinnova (Cykla).
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Autonomous driving safety is becoming a paramount issue due to the emergence of many autonomous vehicle prototypes. The safety measures ensure that autonomous vehicles are safe to operate among pedestrians, cyclists and conventional vehicles. While safety measures for pedestrians have been widely studied in literature, little attention has been paid to safety measures for cyclists. Visual cyclists analysis is a challenging problem due to the complex structure and dynamic nature of the cyclists. The dynamic model used for cyclists analysis heavily relies on the wheels. In this paper, we investigate the problem of ellipse detection for visual cyclists analysis in the wild. Our first contribution is the introduction of a new challenging annotated dataset for bicycle wheels, collected in real-world urban environment. Our second contribution is a method that combines reliable arcs selection and grouping strategies for ellipse detection. The reliable selection and grouping mechanism leads to robust ellipse detections when combined with the standard least square ellipse fitting approach. Our experiments clearly demonstrate that our method provides improved results, both in terms of accuracy and robustness in challenging urban environment settings.
AB - Autonomous driving safety is becoming a paramount issue due to the emergence of many autonomous vehicle prototypes. The safety measures ensure that autonomous vehicles are safe to operate among pedestrians, cyclists and conventional vehicles. While safety measures for pedestrians have been widely studied in literature, little attention has been paid to safety measures for cyclists. Visual cyclists analysis is a challenging problem due to the complex structure and dynamic nature of the cyclists. The dynamic model used for cyclists analysis heavily relies on the wheels. In this paper, we investigate the problem of ellipse detection for visual cyclists analysis in the wild. Our first contribution is the introduction of a new challenging annotated dataset for bicycle wheels, collected in real-world urban environment. Our second contribution is a method that combines reliable arcs selection and grouping strategies for ellipse detection. The reliable selection and grouping mechanism leads to robust ellipse detections when combined with the standard least square ellipse fitting approach. Our experiments clearly demonstrate that our method provides improved results, both in terms of accuracy and robustness in challenging urban environment settings.
UR - http://www.scopus.com/inward/record.url?scp=85028511092&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-64689-3_26
DO - 10.1007/978-3-319-64689-3_26
M3 - Conference contribution
AN - SCOPUS:85028511092
SN - 9783319646886
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 319
EP - 331
BT - Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings
A2 - Heyden, Anders
A2 - Felsberg, Michael
A2 - Kruger, Norbert
PB - Springer Verlag
T2 - 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017
Y2 - 22 August 2017 through 24 August 2017
ER -