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Poster
Learning Motion Disfluencies for Automatic Sign Language Segmentation
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- Citation Author(s):
- Submitted by:
- Heike Brock
- Last updated:
- 9 May 2019 - 2:18am
- Document Type:
- Poster
- Document Year:
- 2019
- Event:
- Presenters:
- Heike Brock
- Paper Code:
- 4135
- Categories:
- Keywords:
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We introduce a novel technique for the automatic detection of word boundaries within continuous sentence expressions in Japanese Sign Language from three-dimensional body joint positions. First, the flow of signed sentence data within a temporal neighborhood is determined utilizing the spatial correlations between line segments of inter-joint pairs. Next, a frame-wise binary random forest classifier is trained to distinguish word and non-word frame content based on the extracted spatio-temporal features. The output of the classifier is used to propose an automatic word synthesis that achieves reliable and accurate sentence segmentation with an average frame-wise F1 score of 0.89. Evaluation with a baseline data set furthermore shows that the proposed approach can easily be adapted to distinguish between motion transitions and motion primitives for a coarse-action domain.
Poster.pdf
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