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 in 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 common camera calibration algorithms 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.

Satellite Markers: a simple method for ground truth car pose on stereo video / Gustavo, Gil; Giovanni, Savino; Simone, Piantini; Marco, Pierini. - ELETTRONICO. - (2017), pp. 1-9. (Intervento presentato al convegno The 10th International Conference on Machine Vision tenutosi a Vienna, Austria nel 13-15 November 2017).

Satellite Markers: a simple method for ground truth car pose on stereo video

Gustavo Gil
;
Giovanni Savino;Simone Piantini;Marco Pierini
2017

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 in 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 common camera calibration algorithms 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.
2017
ICMV Conference - International Conference on Machine Vision
The 10th International Conference on Machine Vision
Vienna, Austria
13-15 November 2017
Gustavo, Gil; Giovanni, Savino; Simone, Piantini; Marco, Pierini
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1108960
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