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Depth-based hand pose estimation : Methods, data, and challenges / James Steven Supančič in International journal of computer vision, vol 126 n° 11 (November 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 11 (November 2018) . - pp 1180 - 1198 Titre : Depth-based hand pose estimation : Methods, data, and challenges Type de document : Article/Communication Auteurs : James Steven Supančič, Auteur ; Grégory Rogez, Auteur ; Yi Yang, Auteur ; Jamie Shotton, Auteur ; Deva Ramanan, Auteur Année de publication : 2018 Article en page(s) : pp 1180 - 1198 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] estimation de pose [Termes IGN] état de l'art [Termes IGN] image RVB [Termes IGN] plus proche voisin, algorithme du Résumé : (Auteur) Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and have released software and evaluation code. We summarize important conclusions here: (1) Coarse pose estimation appears viable for scenes with isolated hands. However, high precision pose estimation [required for immersive virtual reality and cluttered scenes (where hands may be interacting with nearby objects and surfaces) remain a challenge. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress. Numéro de notice : A2018-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1081-7 Date de publication en ligne : 12/04/2018 En ligne : https://doi.org/10.1007/s11263-018-1081-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92523 [article]