Fast Vanishing Point Estimation Based on Particle Swarm Optimization

Xun PAN; Wa SI; Harutoshi OGAI

doi:10.1587/transinf.2015EDP7326

Fast Vanishing Point Estimation Based on Particle Swarm Optimization

Xun PAN, Wa SI, Harutoshi OGAI

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Summary :

Vanishing point estimation is an important issue for vision based road detection, especially in unstructured roads. However, most of the existing methods suffer from the long calculating time. This paper focuses on improving the efficiency of vanishing point estimation by using a heuristic voting method based on particle swarm optimization (PSO). Experiments prove that with our proposed method, the efficiency of vanishing point estimation is significantly improved with almost no loss in accuracy. Moreover, for sequenced images, this method is further improved and can get even better performance, by making full use of inter-frame information to optimize the performance of PSO.

Publication: IEICE TRANSACTIONS on Information Vol.E99-D No.2 pp.505-513

Publication Date: 2016/02/01

Publicized: 2015/11/06

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2015EDP7326

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

Authors

Xun PAN
  Waseda University
Wa SI
  Waseda University
Harutoshi OGAI
  Waseda University

Keyword

vanishing point estimation, particle swarm optimization, inter-frame information

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Xun PAN, Wa SI, Harutoshi OGAI, "Fast Vanishing Point Estimation Based on Particle Swarm Optimization" in IEICE TRANSACTIONS on Information, vol. E99-D, no. 2, pp. 505-513, February 2016, doi: 10.1587/transinf.2015EDP7326.
Abstract: Vanishing point estimation is an important issue for vision based road detection, especially in unstructured roads. However, most of the existing methods suffer from the long calculating time. This paper focuses on improving the efficiency of vanishing point estimation by using a heuristic voting method based on particle swarm optimization (PSO). Experiments prove that with our proposed method, the efficiency of vanishing point estimation is significantly improved with almost no loss in accuracy. Moreover, for sequenced images, this method is further improved and can get even better performance, by making full use of inter-frame information to optimize the performance of PSO.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDP7326/_p

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@ARTICLE{e99-d_2_505,
author={Xun PAN, Wa SI, Harutoshi OGAI, },
journal={IEICE TRANSACTIONS on Information},
title={Fast Vanishing Point Estimation Based on Particle Swarm Optimization},
year={2016},
volume={E99-D},
number={2},
pages={505-513},
abstract={Vanishing point estimation is an important issue for vision based road detection, especially in unstructured roads. However, most of the existing methods suffer from the long calculating time. This paper focuses on improving the efficiency of vanishing point estimation by using a heuristic voting method based on particle swarm optimization (PSO). Experiments prove that with our proposed method, the efficiency of vanishing point estimation is significantly improved with almost no loss in accuracy. Moreover, for sequenced images, this method is further improved and can get even better performance, by making full use of inter-frame information to optimize the performance of PSO.},
keywords={},
doi={10.1587/transinf.2015EDP7326},
ISSN={1745-1361},
month={February},}

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TY - JOUR
TI - Fast Vanishing Point Estimation Based on Particle Swarm Optimization
T2 - IEICE TRANSACTIONS on Information
SP - 505
EP - 513
AU - Xun PAN
AU - Wa SI
AU - Harutoshi OGAI
PY - 2016
DO - 10.1587/transinf.2015EDP7326
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2016
AB - Vanishing point estimation is an important issue for vision based road detection, especially in unstructured roads. However, most of the existing methods suffer from the long calculating time. This paper focuses on improving the efficiency of vanishing point estimation by using a heuristic voting method based on particle swarm optimization (PSO). Experiments prove that with our proposed method, the efficiency of vanishing point estimation is significantly improved with almost no loss in accuracy. Moreover, for sequenced images, this method is further improved and can get even better performance, by making full use of inter-frame information to optimize the performance of PSO.
ER -