Planar Pose Estimation Using Object Detection and Reinforcement Learning

Frederik Nørby Rasmussen, Sebastian Terp Andersen, Bjarne Grossmann, Evangelos Boukas, Lazaros Nalpantidis*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Pose estimation concerns systems or models dealing with the determination of a static object’s pose using, in this case, vision. This paper approaching the problem with an active vision-based solution, that integrates both perception and action in the same model. The problem is solved using a combination of neural networks for object detection and a reinforcement learning architecture for moving a camera and estimating the pose. A robotic implementation of the proposed active vision system is used for testing with promising results. Experiments show that our approach does not only solve the simple task of planar visual pose estimation, but also exhibits robustness to changes in the environment.

Original languageEnglish
Title of host publicationProceedings of 12th International Conference on Computer Vision Systems
EditorsDimitrios Tzovaras, Dimitrios Giakoumis, Markus Vincze, Antonis Argyros
Publication date1 Jan 2019
ISBN (Print)9783030349943
Publication statusPublished - 1 Jan 2019
Event12th International Conference on Computer Vision Systems, ICVS 2019 - Thessaloniki, Greece
Duration: 23 Sep 201925 Sep 2019


Conference12th International Conference on Computer Vision Systems, ICVS 2019
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11754 LNCS


  • Object detection
  • Pose estimation
  • Reinforcement learning


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