Hand detection using multiple proposals

Conference item

We describe a two-stage method for detecting hands and their orientation in unconstrained images. The first stage uses three complementary detectors to propose hand bounding boxes. Each bounding box is then scored by the three detectors independently, and a second stage classifier learnt to compute a final confidence score for the proposals using these features. We make the following contributions: (i) we add context-based and skin-based proposals to a sliding window shape based detector to increase recall; (ii) we develop a new method of non-maximum suppression based on super-pixels; and (iii) we introduce a fully annotated hand dataset for training and testing. We show that the hand detector exceeds the state of the art on two public datasets, including the PASCAL VOC 2010 human layout challenge.

Authors: Mittal, A; Zisserman, A; Torr, PHS

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: British Machine Vision Association
• Host title: Proceedings of the British Machine Vision Conference 2011
• Publication date: 2011-01-01
• Event title: British Machine Vision Conference (BMVC 2011)
• Event location: Dundee
• Event website: https://bmva-archive.org.uk/bmvc/2011/proceedings/frontmatter.html
• Event start date: 2011-08-29
• Event end date: 2011-09-02
• Language: English