| Author | dc.contributor.author | Francke Henríquez, Hardy Einar | |
| Author | dc.contributor.author | Ruiz del Solar, Javier | es_CL |
| Author | dc.contributor.author | Verschae, Rodrigo | es_CL |
| Admission date | dc.date.accessioned | 2012-12-17T20:18:39Z | |
| Available date | dc.date.available | 2012-12-17T20:18:39Z | |
| Publication date | dc.date.issued | 2012-12-17T20:18:39Z | |
| Identifier | dc.identifier.uri | https://repositorio.uchile.cl/handle/2250/125688 | |
| Abstract | dc.description.abstract | In this article a robust and real-time hand gesture detection and
recognition system for dynamic environments is proposed. The system is based
on the use of boosted classifiers for the detection of hands and the recognition
of gestures, together with the use of skin segmentation and hand tracking
procedures. The main novelty of the proposed approach is the use of innovative
training techniques - active learning and bootstrap -, which allow obtaining a
much better performance than similar boosting-based systems, in terms of
detection rate, number of false positives and processing time. In addition, the
robustness of the system is increased due to the use of an adaptive skin model, a
color-based hand tracking, and a multi-gesture classification tree. The system
performance is validated in real video sequences. | es_CL |
| Patrocinador | dc.description.sponsorship | This research was funded by Millenium Nucleus Center for Web Research, Grant
P04-067-F, Chile. | es_CL |
| Lenguage | dc.language.iso | en | es_CL |
| Keywords | dc.subject | Hand gesture recognition | es_CL |
| Título | dc.title | Real-time Hand Gesture Detection and Recognition using Boosted Classifiers and Active Learning | es_CL |
| Document type | dc.type | Artículo de revista | |
