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research-article

Stixels estimation through stereo matching of road scenes

Authors:

Soon Ki JungAuthors Info & Claims

RACS '14: Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems

Pages 116 - 120

https://doi.org/10.1145/2663761.2664218

Published: 05 October 2014 Publication History

Abstract

Recently, Stixel-world, a medium level representation of road scene components has been introduced. The existing stixels estimation approaches are separated from a depth estimation process, or they directly make use of stereo images and only compute stixels without producing per-pixel depth information. For road scenes, however, many machine vision tasks require both per-pixel depth information and the higher-level representation of it.

This paper presents a combined process of stixels estimation and stereo matching process. The proposed method generates per-pixel depth information and stixels for both the ground surface and obstacles, at the same time. We have modified a multi-path line-optimization process of the stereo matching algorithm to produce multiple stixels of the ground and obstacle segments for each image column.

Experimental results show that the proposed algorithm estimates stixels more accurately than the existing algorithm, and it also produces high-quality dense depth information, at the same time.

References

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H. Badino, U. Franke, and D. Pfeiffer. The stixel world a compact medium level representation of the 3d-world. In In German Association for Pattern Recognition (DAGM), 2009.

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C. Banz, S. Hesselbarth, H. Flatt, H. Blume, and P. Pirsch. Real-time stereo vision system using semi-global matching disparity estimation: Architecture and fpga-implementation. In Embedded Computer Systems (SAMOS), 2010 International Conference on, 2010.

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R. Benenson, M. Mathias, R. Timofte, and L. V. Gool. Fast stixel computation for fast pedestrian detection. In ECCV workshop, 2012.

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R. Benenson, R. Timofte, and L. V. Gool. Stixels estimation without depth map computation. In ICCV Workshop, 2011.

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F. Erbs, B. Schwarz, and U. Franke. Stixmentation-probabilistic stixel based traffic scene labeling. In BMVC, 2012.

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B. Gunyel, R. Benenson, R. Timofte, and L. V. Gool. Stixels motion estimation without optical flow computation. In ECCV, 2012.

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H. Hattori, A. Seki, M. Nishiyama, and T. Watanabe. Stereo-based pedestrian detection using multiple patterns. In BMVC, 2009.

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S. Hermann and R. Klette. Iterative semi-global matching for robust driver assistance systems. In ACCV, 2012.

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A. Hosni, M. Bleyer, C. Rother, and M. Gelautz. Fast cost-volume filtering for visual correspondence and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(2):504--511, February 2013.

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J. Lu, H. Yang, D. Min, and M. N. Do. Patchmatch filter: Efficient edge-aware filtering meets randomized search for fast correspondence fidel estimation. In CVPR, 2013.

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D. Pfeiffer and U. Franke. Towards a global optimal multi-layer stixel representation of dense 3d data. In BMVC, 2011.

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K. H. Won and S. K. Jung. Billboard sweep stereo for obstacle detection in road scenes. Electronics Letters, 48(24):1528--1530, November 2012.

Cited By

Blaga BNedevschi S(2018)A Method for Automatic Pole Detection from Urban Video Scenes using Stereo Vision2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)10.1109/ICCP.2018.8516640(293-300)Online publication date: Sep-2018
https://doi.org/10.1109/ICCP.2018.8516640
Carrillo DSutherland A(2016)Fast Obstacle Detection Using Sparse Edge-Based Disparity Maps2016 Fourth International Conference on 3D Vision (3DV)10.1109/3DV.2016.80(66-72)Online publication date: Oct-2016
https://doi.org/10.1109/3DV.2016.80
Ding DKwon SPark JJung W(2015)Stereo vision-based obstacle detection using fusion method of road scenesTENCON 2015 - 2015 IEEE Region 10 Conference10.1109/TENCON.2015.7372736(1-2)Online publication date: Nov-2015
https://doi.org/10.1109/TENCON.2015.7372736

Index Terms

Stixels estimation through stereo matching of road scenes
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding

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Information

Published In

cover image ACM Conferences

RACS '14: Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems

October 2014

386 pages

ISBN:9781450330602

DOI:10.1145/2663761

Conference Chairs:
Chao Lu
Towson University
,
Esmaeil Nadimi
University of Southern Denmark, denmark
,
Program Chairs:
Sung-Ryul Kim
Konkuk University, Korea
,
Wei Wang
San Diego State University

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Winway Systems: Winway Systems, Inc.
SIGAPP: ACM Special Interest Group on Applied Computing
ACCT: Association of Convergent Computing Technology
TU: Towson University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 October 2014

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RACS '14

Sponsor:

Winway Systems
SIGAPP
ACCT
TU

RACS '14: International Conference on Research in Adaptive and Convergent Systems

October 5 - 8, 2014

Maryland, Towson

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RACS '14 Paper Acceptance Rate 59 of 251 submissions, 24%;

Overall Acceptance Rate 393 of 1,581 submissions, 25%

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Cited By

Blaga BNedevschi S(2018)A Method for Automatic Pole Detection from Urban Video Scenes using Stereo Vision2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)10.1109/ICCP.2018.8516640(293-300)Online publication date: Sep-2018
https://doi.org/10.1109/ICCP.2018.8516640
Carrillo DSutherland A(2016)Fast Obstacle Detection Using Sparse Edge-Based Disparity Maps2016 Fourth International Conference on 3D Vision (3DV)10.1109/3DV.2016.80(66-72)Online publication date: Oct-2016
https://doi.org/10.1109/3DV.2016.80
Ding DKwon SPark JJung W(2015)Stereo vision-based obstacle detection using fusion method of road scenesTENCON 2015 - 2015 IEEE Region 10 Conference10.1109/TENCON.2015.7372736(1-2)Online publication date: Nov-2015
https://doi.org/10.1109/TENCON.2015.7372736

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