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Real-time accurate 3D head tracking and pose estimation with consumer RGB-D cameras / David Joseph Tan in International journal of computer vision, vol 126 n° 2-4 (April 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 2-4 (April 2018) . - pp 158 - 183 Titre : Real-time accurate 3D head tracking and pose estimation with consumer RGB-D cameras Type de document : Article/Communication Auteurs : David Joseph Tan, Auteur ; Federico Tombari, Auteur ; Nassir Navab, Auteur Année de publication : 2018 Article en page(s) : pp 158 - 183 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] détection de visage [Termes IGN] données localisées 3D [Termes IGN] estimation de pose [Termes IGN] image RVB [Termes IGN] méthode robuste [Termes IGN] séquence d'images [Termes IGN] temps réel Résumé : (Auteur) We demonstrate how 3D head tracking and pose estimation can be effectively and efficiently achieved from noisy RGB-D sequences. Our proposal leverages on a random forest framework, designed to regress the 3D head pose at every frame in a temporal tracking manner. One peculiarity of the algorithm is that it exploits together (1) a generic training dataset of 3D head models, which is learned once offline; and, (2) an online refinement with subject-specific 3D data, which aims for the tracker to withstand slight facial deformations and to adapt its forest to the specific characteristics of an individual subject. The combination of these works allows our algorithm to be robust even under extreme poses, where the user’s face is no longer visible on the image. Finally, we also propose another solution that utilizes a multi-camera system such that the data simultaneously acquired from multiple RGB-D sensors helps the tracker to handle challenging conditions that affect a subset of the cameras. Notably, the proposed multi-camera frameworks yields a real-time performance of approximately 8 ms per frame given six cameras and one CPU core, and scales up linearly to 30 fps with 25 cameras. Numéro de notice : A2018-406 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-017-0988-8 Date de publication en ligne : 02/02/2017 En ligne : https://doi.org/10.1007/s11263-017-0988-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90879 [article]