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Stixel Based Scene Understanding for Autonomous Vehicles

Wieszok, Z., Aouf, N., Kechagias-Stamatis, O. and Chermak, L. (2017). Stixel Based Scene Understanding for Autonomous Vehicles. 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), pp. 43-49. doi: 10.1109/ICNSC.2017.8000065


We propose a stereo vision based obstacle detection and scene segmentation algorithm appropriate for autonomous vehicles. Our algorithm is based on an innovative extension of the Stixel world, which neglects computing a disparity map. Ground plane and stixel distance estimation is improved by exploiting an online learned color model. Furthermore, the stixel height estimation is leveraged by an innovative joined membership scheme based on color and disparity information. Stixels are then used as an input for the semantic scene segmentation providing scene understanding, which can be further used as a comprehensive middle level representation for high-level object detectors.

Publication Type: Article
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.
Publisher Keywords: Dynamic Programming; Obstacle Detection; Stereo Vision; Semantic Segmentation; Stixel World
Subjects: H Social Sciences > HE Transportation and Communications
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
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