Visual Landmark Based 3D Road Course Estimation with Black Box Variational Inference

Felix Trusheim; Alexandru Condurache; Alfred Mertins

doi:10.1007/978-3-319-64689-3_27

Visual Landmark Based 3D Road Course Estimation with Black Box Variational Inference

Felix Trusheim, Alexandru Condurache, Alfred Mertins

Institute of Signal Processing

Abstract

In this paper we present an approach which estimates the course of a road over long distances based on static and dynamic scene cues detected by a video camera. The approach is based on a clothoid road model, a probabilistic fusion concept as well as a fast variational inference method. Our experimental results show that the approach outperforms a state-of-the-art road marking-based method in challenging real-world driving situations.

Original language	English
Title of host publication	Computer Analysis of Images and Patterns
Editors	Michael Felsberg, Anders Heyden, Norbert Krüger
Number of pages	12
Volume	10424
Publisher	Springer International Publishing
Publication date	28.07.2017
Pages	332-343
ISBN (Print)	978-3-319-64688-6
ISBN (Electronic)	978-3-319-64689-3
DOIs	https://doi.org/10.1007/978-3-319-64689-3_27
Publication status	Published - 28.07.2017
Event	17th International Conference on Computer Analysis of Images and Patterns - Ystad, Sweden Duration: 22.08.2017 → 24.08.2017 Conference number: 196269

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Trusheim, F., Condurache, A., & Mertins, A. (2017). Visual Landmark Based 3D Road Course Estimation with Black Box Variational Inference. In M. Felsberg, A. Heyden, & N. Krüger (Eds.), Computer Analysis of Images and Patterns (Vol. 10424, pp. 332-343). ( Lecture Notes in Computer Science; Vol. 10424 LNCS). Springer International Publishing. https://doi.org/10.1007/978-3-319-64689-3_27

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title = "Visual Landmark Based 3D Road Course Estimation with Black Box Variational Inference",

abstract = "In this paper we present an approach which estimates the course of a road over long distances based on static and dynamic scene cues detected by a video camera. The approach is based on a clothoid road model, a probabilistic fusion concept as well as a fast variational inference method. Our experimental results show that the approach outperforms a state-of-the-art road marking-based method in challenging real-world driving situations.",

author = "Felix Trusheim and Alexandru Condurache and Alfred Mertins",

year = "2017",

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day = "28",

doi = "10.1007/978-3-319-64689-3_27",

language = "English",

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pages = "332--343",

editor = "Michael Felsberg and Anders Heyden and Norbert Kr{\"u}ger",

booktitle = "Computer Analysis of Images and Patterns",

note = "17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017 ; Conference date: 22-08-2017 Through 24-08-2017",

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Trusheim, F, Condurache, A & Mertins, A 2017, Visual Landmark Based 3D Road Course Estimation with Black Box Variational Inference. in M Felsberg, A Heyden & N Krüger (eds), Computer Analysis of Images and Patterns. vol. 10424, Lecture Notes in Computer Science, vol. 10424 LNCS, Springer International Publishing, pp. 332-343, 17th International Conference on Computer Analysis of Images and Patterns, Ystad, Sweden, 22.08.17. https://doi.org/10.1007/978-3-319-64689-3_27

Visual Landmark Based 3D Road Course Estimation with Black Box Variational Inference. / Trusheim, Felix; Condurache, Alexandru; Mertins, Alfred.
Computer Analysis of Images and Patterns. ed. / Michael Felsberg; Anders Heyden; Norbert Krüger. Vol. 10424 Springer International Publishing, 2017. p. 332-343 ( Lecture Notes in Computer Science; Vol. 10424 LNCS).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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N2 - In this paper we present an approach which estimates the course of a road over long distances based on static and dynamic scene cues detected by a video camera. The approach is based on a clothoid road model, a probabilistic fusion concept as well as a fast variational inference method. Our experimental results show that the approach outperforms a state-of-the-art road marking-based method in challenging real-world driving situations.

AB - In this paper we present an approach which estimates the course of a road over long distances based on static and dynamic scene cues detected by a video camera. The approach is based on a clothoid road model, a probabilistic fusion concept as well as a fast variational inference method. Our experimental results show that the approach outperforms a state-of-the-art road marking-based method in challenging real-world driving situations.

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SN - 978-3-319-64688-6

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EP - 343

BT - Computer Analysis of Images and Patterns

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T2 - 17th International Conference on Computer Analysis of Images and Patterns

Y2 - 22 August 2017 through 24 August 2017

ER -

Visual Landmark Based 3D Road Course Estimation with Black Box Variational Inference

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