Saleem, NH, Rezaei, M orcid.org/0000-0003-3892-421X and Klette, R (2017) Extending the stixel world using polynomial ground manifold approximation. In: 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 21-23 Nov 2017, Auckland, New Zealnd. IEEE ISBN 978-1-5090-6546-2
Abstract
Stixel-based segmentation is specifically designed towards obstacle detection which combines road surface estimation in traffic scenes, stixel calculations, and stixel clustering. Stixels are defined by observed height above road surface. Road surfaces (ground manifolds) are represented by using an occupancy grid map. Stixel-based segmentation may improve the accuracy of real-time obstacle detection, especially if adaptive to changes in ground manifolds (e.g. with respect to non-planar road geometry). In this paper, we propose the use of a polynomial curve fitting algorithm based on the v-disparity space for ground manifold estimation. This is beneficial for two reasons. First, the coordinate space has inherently finite boundaries, which is useful when working with probability densities. Second, it leads to reduced computation time. We combine height segmentation and improved ground manifold algorithms together for stixel extraction. Our experimental results show a significant improvement in the accuracy of the ground manifold detection (an 8% improvement) compared to occupancy-grid mapping methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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: | 21 Feb 2020 13:46 |
Last Modified: | 30 Jun 2020 21:28 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/m2vip.2017.8211440 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157459 |