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Documents authored by Belmecheri, Nassim

Artifact
Software
DOI: 10.4230/artifacts.24755
QualiNet

Authors: Nassim Belmecheri

Abstract
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Nassim Belmecheri. QualiNet (Software, Source code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-24755,
   title = {{QualiNet}}, 
   author = {Belmecheri, Nassim},
   note = {Software, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:a4900663aeb84632699b0217f7a7f98014466c00;origin=https://github.com/nassimbel/QualiNet;visit=swh:1:snp:425d9e3015b7c13a2d10dfb966d88146d85121d7;anchor=swh:1:rev:95ded8256592cacf31eeca422dd61aa8515f74ad}{\texttt{swh:1:dir:a4900663aeb84632699b0217f7a7f98014466c00}} (visited on 2025-10-13)},
   url = {https://github.com/nassimbel/QualiNet.git},
   doi = {10.4230/artifacts.24755},
}
Document
Short Paper
DOI: 10.4230/LIPIcs.TIME.2025.14
QualiNet: Acquiring Bird’s Eye View Qualitative Spatial Representation from 2D Images in Automated Vehicle Perception (Short Paper)

Authors: Nassim Belmecheri

Published in: LIPIcs, Volume 355, 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)

Abstract
We present QualiNet, an end-to-end deep learning framework that acquires Bird’s Eye View (BEV) qualitative spatial relations directly from 2D images, eliminating the need for depth sensors. The system combines 2D object detection, masking, and classification to infer Rectangle Algebra (RA) and Qualitative Distance Calculus (QDC) relations. Evaluated on NuScenes and PandaSet datasets, QualiNet achieves 91% accuracy for RA, 80% for QDC, and 99% top-2 accuracy, demonstrating robust performance for automated vehicle perception.
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Nassim Belmecheri. QualiNet: Acquiring Bird’s Eye View Qualitative Spatial Representation from 2D Images in Automated Vehicle Perception (Short Paper). In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 14:1-14:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{belmecheri:LIPIcs.TIME.2025.14,
  author =	{Belmecheri, Nassim},
  title =	{{QualiNet: Acquiring Bird’s Eye View Qualitative Spatial Representation from 2D Images in Automated Vehicle Perception}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{14:1--14:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-401-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{355},
  editor =	{Vidal, Thierry and Wa{\l}\k{e}ga, Przemys{\l}aw Andrzej},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2025.14},
  URN =		{urn:nbn:de:0030-drops-244608},
  doi =		{10.4230/LIPIcs.TIME.2025.14},
  annote =	{Keywords: Qualitative Spatial Representation, Deep Learning, Computer vision, Qualitative Scene Understanding, Spatio-temporal representation and reasoning models (including moving objects tracking)}
}
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