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Ellipse Detection for Visual Cyclists Analysis “In the Wild”
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-6096-3648
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
2017 (engelsk)Inngår i: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, Vol. 10424, s. 319-331Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Springer, 2017. Vol. 10424, s. 319-331
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-145372DOI: 10.1007/978-3-319-64689-3_26ISI: 000432085900026ISBN: 9783319646886 (tryckt)ISBN: 9783319646893 (digital)OAI: oai:DiVA.org:liu-145372DiVA, id: diva2:1185617
Konferanse
17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I
Merknad

Funding agencies: VR (EMC2, ELLIIT, starting grant) [2016-05543]; Vinnova (Cykla)

Tilgjengelig fra: 2018-02-26 Laget: 2018-02-26 Sist oppdatert: 2018-10-17bibliografisk kontrollert

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