Szczegóły publikacji
Opis bibliograficzny
Fiducial points-supported object pose tracking on RGB images via particle filtering with heuristic optimization / Mateusz MAJCHER, Bogdan KWOLEK // W: VISIGRAPP 2021 [Dokument elektroniczny] : proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Vol. 5, VISAPP / eds. Giovanni Maria Farinella, [et al.]. — [Lisbon] : SCITEPRESS - Science and Technology Publications, [2021]. — (VISIGRAPP ; ISSN 2184-5921). — e-ISBN: 978-989-758-488-6. — S. 919–926. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://www.scitepress.org/Link.aspx?doi=10.5220/001023710919... [2021-07-23]. — Bibliogr., Abstr. — Dostęp do pełnego tekstu po zalogowaniu
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 135376 |
|---|---|
| Data dodania do BaDAP | 2021-09-24 |
| DOI | 10.5220/0010237109190926 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Konferencja | Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2021 |
| Czasopismo/seria | VISIGRAPP |
Abstract
We present an algorithm for tracking 6D pose of the object in a sequence of RGB images. The images are acquired by a calibrated camera. A particle filter is utilized to estimate the posterior probability distribution of the object poses. The probabilistic observation model is built on the projected 3D model onto image and then matching the rendered object with the segmented object. It is determined using object silhouette and distance transform-based edge scores. A hypothesis about 6D object pose that is calculated on the basis of object keypoints and the PnP algorithm is included in the probability distribution. A k-means++ algorithm is then executed on multi-modal probability distribution to determine modes. A multi-swarm particle swarm optimization is executed afterwards to find finest modes in the probability distribution together with the best pose. The object of interest is segmented by an U-Net neural network. Eight fiducial points of the object are determined by a neural network. A data generator employing 3D object models has been developed to synthesize photorealistic images with ground-truth data for training neural networks both for object segmentation and estimation of keypoints. The 6D object pose tracker has been evaluated both on synthetic and real images. We demonstrate experimentally that object pose hypotheses calculated on the basis of fiducial points and the PnP algorithm lead to considerable improvements in tracking accuracy.
