Anzeige
Erschienen in:
2012 | OriginalPaper | Buchkapitel
Real-Time Hand Gesture Detection and Recognition by Random Forest
verfasst von : Xian Zhao, Zhan Song, Jian Guo, Yanguo Zhao, Feng Zheng
Erschienen in: Communications and Information Processing
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Detection and recognition of an unconstrained hand in a natural video sequence has gained wide applications in HCI (human computer interaction). This paper presents an unsupervised approach for the training of an efficient and robust hand gesture detector. Different with traditional hand feature descriptors, the proposed approach use pair-patch comparison features to describe the samples. And the random forest is introduced to establish a machine learning model. The pair-patch comparison features could rapidly describe a sample and the distributions of them have some similarity between the same classes. In the training procedure, a database which consists of a large number of hand images with corresponding labels and background images are established. Experimental results show that the proposed approach can achieve a detection and accuracy rate of 92.23% on the dataset.