Hand pose estimation using hierarchical detection

Conference item

This paper presents an analysis of the design of classifiers for use in a hierarchical object recognition approach. In this approach, a cascade of classifiers is arranged in a tree in order to recognize multiple object classes. We are interested in the problem of recognizing multiple patterns as it is closely related to the problem of locating an articulated object. Each different pattern class corresponds to the hand in a different pose, or set of poses. For this problem obtaining labelled training data of the hand in a given pose can be problematic. Given a parametric 3D model, generating training data in the form of example images is cheap, and we demonstate that it can be used to design classifiers almost as good as those trained using non-synthetic data. We compare a variety of different template-based classifiers and discuss their merits.

Authors: Stenger, B; Thayananthan, A; Torr, PHS; Cipolla, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Host title: Computer Vision in Human-Computer Interaction: ECCV 2004 Workshop on HCI, Prague, Czech Republic, May 16, 2004, Proceedings
• Pages: 105-116
• Series: Lecture Notes in Computer Science
• Series number: 3058
• Place of publication: Berlin, Heidelberg
• Publication date: 2004-05-12
• Event title: ECCV 2004 Workshop on HCI
• Event location: Prague, Czech Republic
• Event start date: 2004-05-16
• Event end date: 2004-05-16
• DOI: 10.1007/978-3-540-24837-8_11
• EISSN: 1611-3349
• ISSN: 0302-9743
• EISBN: 9783540248378
• ISBN: 9783540220121
• Language: English