Mixing deep learning with classical vision for object recognition

Maciej Stefańczyk , Tomasz Bocheński


Nowadays, when one needs a system for image recognition, it is mostly a matter of finding pre-trained CNN and, sometimes, adding additional training based on transferred knowledge. Accurate 6-DOF object localization in the image is a more laborious task and requires more complex training data to be available. On the other hand, if we know the model of the object, it is straightforward to acquire its pose from the image (RGB or RGB-D). In this paper, we try to show the advantages of mixing deep learning object recognition/detection with classical 6-DOF pose estimation algorithms, with a focus on applications in service robotics
Author Maciej Stefańczyk (FEIT / AK)
Maciej Stefańczyk,,
- The Institute of Control and Computation Engineering
, Tomasz Bocheński (FEIT / AK)
Tomasz Bocheński,,
- The Institute of Control and Computation Engineering
Journal seriesJournal of WSCG, ISSN 1213-6972, e-ISSN 1213-6964, [1213-6964, 1213-6980]
Issue year2020
Publication size in sheets0.5
Keywords in EnglishCNN object detection, VGG16, ResNet50, 6-DOF pose estimation, RanSaC, ICP, RGB-D
ASJC Classification1704 Computer Graphics and Computer-Aided Design; 1712 Software; 2605 Computational Mathematics
URL http://wscg.zcu.cz/wscg2020/journal/G89.pdf
ProjectUsing spherical cameras in service robotics. Project leader: Stefańczyk Maciej, , end date 31-12-2018, dziek MStef 2018, Completed
Languageen angielski
Stefanczyk Bochenski WSCG20.pdf 2.26 MB
Score (nominal)40
Score sourcejournalList
ScoreMinisterial score = 40.0, 22-09-2020, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2018 = 0.425
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