论文标题
在手语识别中对艺术状态的定量调查
Quantitative Survey of the State of the Art in Sign Language Recognition
论文作者
论文摘要
这项工作提出了一项元研究，涵盖了大约300张发表的手语识别论文，并具有超过400个实验结果。它包括1983年至2020年该领域开始之间的大多数论文。此外，它涵盖了对25多种研究的细粒度分析，这些分析比较了他们对该领域的标准基准任务RWTH-Phoenix-Weather 2014的识别方法。在过去的十年中，在手语识别领域的研究取得了显着进展，达到了一项比以往任何时候都更加引起关注的地步。这项研究以简洁的方式汇编了艺术状况，以帮助推进该领域并揭示开放的问题。此外，所有这些元研究的源数据都公开了，从而减轻了未来的工作并进一步扩展。分析的论文已用一组类别手动标记。这些数据揭示了许多见解，例如，诸如该领域的许多见解从侵入性捕获到非侵入性捕获，从本地特征到全局特征，以及在中等和更大的词汇识别系统中包含的非人工参数。令人惊讶的是，带有1080个标志的词汇量的RWTh-Phoenix-Weather代表了大型词汇连续语言识别的唯一资源。
This work presents a meta study covering around 300 published sign language recognition papers with over 400 experimental results. It includes most papers between the start of the field in 1983 and 2020. Additionally, it covers a fine-grained analysis on over 25 studies that have compared their recognition approaches on RWTH-PHOENIX-Weather 2014, the standard benchmark task of the field. Research in the domain of sign language recognition has progressed significantly in the last decade, reaching a point where the task attracts much more attention than ever before. This study compiles the state of the art in a concise way to help advance the field and reveal open questions. Moreover, all of this meta study's source data is made public, easing future work with it and further expansion. The analyzed papers have been manually labeled with a set of categories. The data reveals many insights, such as, among others, shifts in the field from intrusive to non-intrusive capturing, from local to global features and the lack of non-manual parameters included in medium and larger vocabulary recognition systems. Surprisingly, RWTH-PHOENIX-Weather with a vocabulary of 1080 signs represents the only resource for large vocabulary continuous sign language recognition benchmarking world wide.
