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Proceedings Paper

Satellite markers: a simple method for ground truth car pose on stereo video
Author(s): Gustavo Gil; Giovanni Savino; Simone Piantini; Marco Pierini
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Paper Abstract

Artificial prediction of future location of other cars in the context of advanced safety systems is a must. The remote estimation of car pose and particularly its heading angle is key to predict its future location. Stereo vision systems allow to get the 3D information of a scene. Ground truth in this specific context is associated with referential information about the depth, shape and orientation of the objects present in the traffic scene. Creating 3D ground truth is a measurement and data fusion task associated with the combination of different kinds of sensors. The novelty of this paper is the method to generate ground truth car pose only from video data. When the method is applied to stereo video, it also provides the extrinsic camera parameters for each camera at frame level which are key to quantify the performance of a stereo vision system when it is moving because the system is subjected to undesired vibrations and/or leaning. We developed a video post-processing technique which employs a common camera calibration tool for the 3D ground truth generation. In our case study, we focus in accurate car heading angle estimation of a moving car under realistic imagery. As outcomes, our satellite marker method provides accurate car pose at frame level, and the instantaneous spatial orientation for each camera at frame level.

Paper Details

Date Published: 13 April 2018
PDF: 10 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 1069620 (13 April 2018); doi: 10.1117/12.2309577
Show Author Affiliations
Gustavo Gil, Univ. degli Studi di Firenze (Italy)
Giovanni Savino, Univ. degli Studi di Firenze (Italy)
Monash Univ. (Australia)
Simone Piantini, Univ. degli Studi di Firenze (Italy)
Marco Pierini, Univ. degli Studi di Firenze (Italy)


Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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