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Randomized decision forests for static and dynamic hand shape classification.
- Source :
- 2012 IEEE Computer Society Conference on Computer Vision & Pattern Recognition Workshops; 1/ 1/2012, p31-36, 6p
- Publication Year :
- 2012
-
Abstract
- This paper proposes a novel algorithm to perform hand shape classification using depth sensors, without relying on color or temporal information. Hence, the system is independent of lighting conditions and does not need a hand registration step. The proposed method uses randomized classification forests (RDF) to assign class labels to each pixel on a depth image, and the final class label is determined by voting. This method is shown to achieve 97.8% success rate on an American Sign Language (ASL) dataset consisting of 65k images collected from five subjects with a depth sensor. More experiments are conducted on a subset of the ChaLearn Gesture Dataset, consisting of a lexicon with static and dynamic hand shapes. The hands are found using motion cues and cropped using depth information, with a precision rate of 87.88% when there are multiple gestures, and 94.35% when there is a single gesture in the sample. The hand shape classification success rate is 94.74% on a small subset of nine gestures corresponding to a single lexicon. The success rate is 74.3% for the leave-one-subject-out scheme, and 67.14% when training is conducted on an external dataset consisting of the same gestures. The method runs on the CPU in real-time, and is capable of running on the GPU for further increase in speed. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISBNs :
- 9781467316118
- Database :
- Complementary Index
- Journal :
- 2012 IEEE Computer Society Conference on Computer Vision & Pattern Recognition Workshops
- Publication Type :
- Conference
- Accession number :
- 86547032
- Full Text :
- https://doi.org/10.1109/CVPRW.2012.6239183