Saleem, NH, Chien, H-J, Rezaei, M orcid.org/0000-0003-3892-421X et al. (1 more author) (2017) Improved Stixel Estimation Based on Transitivity Analysis in Disparity Space. In: Computer Analysis of Images and Patterns (Lecture Notes in Computer Science series). International Conference on Computer Analysis of Images and Patterns, 22-24 Aug 2017, Ystad, Sweden. Springer , pp. 28-40. ISBN 9783319646886
Abstract
We present a novel method for stixel construction using a calibrated collinear trinocular vision system. Our method takes three conjugate stereo images at the same time to measure the consistency of disparity values by means of the transitivity error in disparity space. Unlike previous stixel estimation methods that are built based on a single disparity map, our proposed method introduces a multi-map fusion technique to obtain more robust stixel calculations. We also apply a polynomial curve fitting approach to detect an accurate road manifold, using the v-disparity space which is built based on a confidence map, which further supports accurate stixel calculation. Comparing the depth information from the extracted stixels (using stixel maps) with depth measurements obtained from a highly accurate LiDAR range sensor, we evaluate the accuracy of the proposed method. Experimental results indicate a significant improvement of 13.6% in the accuracy of stixel detection compared to conventional binocular vision.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing AG 2017. This is an author produced version of a paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Feb 2020 12:24 |
Last Modified: | 26 Feb 2020 15:12 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-319-64689-3_3 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157456 |