Szczegóły publikacji
Opis bibliograficzny
Averaging three-dimensional time-varying sequences of rotations: application to preprocessing of motion capture data / Tomasz Hachaj, Marek R. OGIELA, Marcin Piekarczyk, Katarzyna KOPTYRA // W: Image Analysis : 20th Scandinavian Conference, SCIA 2017 : Tromsø, Norway, June 12–14, 2017 : proceedings, Pt. 1 / eds. Puneet Sharma, Filippo Maria Bianchi. — Cham : Springer International Publishing AG, cop. 2017. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 10269). — ISBN: 978-3-319-59125-4; e-ISBN: 978-3-319-59126-1. — S. 17–28. — Bibliogr. s. 27–28, Abstr.
Autorzy (4)
- Hachaj Tomasz
- AGHOgiela Marek
- Piekarczyk Marcin
- AGHKoptyra Katarzyna
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 111286 |
|---|---|
| Data dodania do BaDAP | 2018-01-22 |
| DOI | 10.1007/978-3-319-59126-1_2 |
| Rok publikacji | 2017 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 20th Scandinavian Conference on Image Analysis |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
The aim of this paper is to propose and initially evaluate our novel algorithm which enables averaging of time-varying sequences of rotations with three degrees of freedom described by quaternions. The methodology is based on Dynamic Time Warping barycenter averaging (DBA) with one minus dot product distance function, Markley’s quaternions averaging method and Gaussian quaternion signal smoothing. The proposed algorithm was successfully applied to generate single, averaged motion capture recording (MoCap) from ten MoCap of mawashi-geri karate kick of black belt Shorin-Ryu karate master. We have used inverse kinematic model. In our experiment mean DTW normalized distance between averaged signal and original signals varied from 0.713 · 10−3 for Hips sensor to 6.153 · 10−3 for LeftForearm sensor, which were very good results. Also the visualization of the averaged MoCap data showed that the proposed method did not introduce unwanted disturbances and may be usable for that task. That type of averaging has many important applications. For example it can be used to calculate and visualize an average performance of an athlete who performs some activity that he wants to optimize during training. The numerical and visual data may be a very important feedback for coach that supervises the training. Also our method is not limited to MoCap data averaging; it can be applied to average any type of quaternion-based time-varying sequences. © Springer International Publishing AG 2017.