Semi-automation of gesture annotation by machine learning and human collaboration

نویسندگان

چکیده

Abstract Gesture and multimodal communication researchers typically annotate video data manually, even though this can be a very time-consuming task. In the present work, method to detect gestures is proposed as fundamental step towards semi-automatic gesture annotation tool. The applied RGB videos requires annotations of part input. technique deploys pose estimation active learning. experiment, it shown that if about 27% annotated, remaining parts annotated automatically with an F-score at least 0.85. Users run tool small number first. If predicted for remainder are not satisfactory, users add further again. code has been released so other practitioners use results research. This confirmed work in conjunction ELAN.

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ژورنال

عنوان ژورنال: Language Resources and Evaluation

سال: 2022

ISSN: ['1574-020X', '1574-0218']

DOI: https://doi.org/10.1007/s10579-022-09586-4