| Summary: | <p>In this work, we propose a framework that enables collection
of large-scale, diverse sign language datasets that can be used
to train automatic sign language recognition models.</p>
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<p>The first contribution of this work is SDTRACK, a generic
method for signer tracking and diarisation in the wild. Our
second contribution is to show how SDTRACK can be used
to automatically annotate 90 hours of British Sign Language
(BSL) content featuring a wide range of signers, and including interviews, monologues and debates. Using SDTRACK,
this data is annotated with 35K active signing tracks, with
corresponding video-level signer identifiers and subtitles, and
40K automatically localised sign labels.</p>
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