Szczegóły publikacji
Opis bibliograficzny
3D model-based 6D object pose tracking on RGB images using particle filtering and heuristic optimization / Mateusz MAJCHER, Bogdan KWOLEK // W: VISIGRAPP 2020 [Dokument elektroniczny] : proceedings of the 15th international joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Vol. 5, VISAPP / eds. Giovanni Maria Farinella, Petia Radeva, Jose Braz. — Wersja do Windows. — Dane tekstowe. — [Lisbon] : SCITEPRESS - Science and Technology Publications, cop. 2020. — (VISIGRAPP ; ISSN 2184-5921). — e-ISBN: 978-989-758-402-2. — S. 690–697. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://www.scitepress.org/PublicationsDetail.aspx?ID=Sc6i5xP... [2020-03-30]. — Bibliogr. s. 697, Abstr. — Dostęp po zalogowaniu
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 128181 |
|---|---|
| Data dodania do BaDAP | 2020-05-05 |
| DOI | 10.5220/0009365706900697 |
| Rok publikacji | 2020 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Konferencja | Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2020 |
| Czasopismo/seria | VISIGRAPP |
Abstract
We present algorithm for tracking 6D pose of the object in a sequence of RGB images. The images are acquired by a calibrated camera. The object of interest is segmented by an U-Net neural network. The network is trained in advance to segment a set of objects from the background. The 6D pose of the object is estimated through projecting the 3D model to image and then matching the rendered object with the segmented object. The objective function is calculated using object silhouette and edge scores determined on the basis of distance transform. A particle filter is used to estimate the posterior probability distribution. A k-means++ algorithm, which applies a sequentially random selection strategy according to a squared distance from the closest center already selected is executed on particles representing multi-modal probability distribution. A particle swarm optimization is then used to find the modes in the probability distribution. Results achieved by the proposed algorithm were compared with results obtained by a particle filter and a particle swarm optimization.
