An online probabilistic road intersection detector

Published in 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017

Recommended citation: ballardini2017

Abstract. In this paper we propose a probabilistic approach for detecting and classifying urban road intersections from a moving vehicle. The approach is based on images from an onboard stereo rig; it relies on the detection of the road ground plane on one side, and on a pixel-level classification of the road on the other. The two processing pipelines are then integrated and the parameters of the road components, i.e., the intersection geometry, are inferred. As opposed to other state-of-the-art offline methods, which require processing of the whole video sequence, our approach integrates the image data by means of an online procedure. The experiments have been performed on well-known KITTI datasets, allowing for future comparisons.

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Recommended citation: @INPROCEEDINGS{7989030, author={A. L. {Ballardini} and D. {Cattaneo} and S. {Fontana} and D. G. {Sorrenti}}, booktitle={2017 IEEE International Conference on Robotics and Automation (ICRA)}, title={An online probabilistic road intersection detector}, year={2017}, volume={}, number={}, pages={239-246}, keywords={geometry;image classification;image sequences;probability;road vehicles;traffic engineering computing;video signal processing;online probabilistic road intersection detector;urban road intersections classification;moving vehicle;onboard stereo rig;pixel-level classification;road components;intersection geometry;video sequence;KITTI datasets;Roads;Three-dimensional displays;Image reconstruction;Pipelines;Probabilistic logic;Detectors;Geometry}, doi={10.1109/ICRA.2017.7989030}, ISSN={null}, month={May},}