手语识别中运动扩展问题的增强层次构建算法

Ruiduo Yang, Sudeep Sarkar, B. Loeding
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引用次数: 62

摘要

自动手语识别的难点之一是动作扩展问题。动作延伸是连接两个连续手势的手势动作。这种影响可能持续很长时间,并且涉及手的形状、位置和运动的变化,因此很难明确地对这些中间部分进行建模。当试图将单个符号与完整的符号句子相匹配时,这就产生了一个问题,因为对于与这些mes相对应的句子的许多块,我们没有模型。我们提出了一种基于动态规划框架版本的方法,称为水平构建,在存在运动扩展(me)的情况下,将符号与连续的手语句子同时分割和匹配。我们增强了经典的关卡构建框架,这样它就可以容纳我们没有明确模型的标签。然后将这种增强的关卡构建算法与三重语法模型相结合,以最佳地分割和标记手语句子。我们使用连续手语句子的单视图视频数据集证明了该算法的有效性。使用增强的level Building方法,我们获得了83%的单词级别识别率,而在相同的数据集上使用经典的level Building框架,识别率为20%。所提出的方法是新颖的,因为它不需要明确的运动扩展模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Level Building Algorithm for the Movement Epenthesis Problem in Sign Language Recognition
One of the hard problems in automated sign language recognition is the movement epenthesis (me) problem. Movement epenthesis is the gesture movement that bridges two consecutive signs. This effect can be over a long duration and involve variations in hand shape, position, and movement, making it hard to explicitly model these intervening segments. This creates a problem when trying to match individual signs to full sign sentences since for many chunks of the sentence, corresponding to these mes, we do not have models. We present an approach based on version of a dynamic programming framework, called Level Building, to simultaneously segment and match signs to continuous sign language sentences in the presence of movement epenthesis (me). We enhance the classical Level Building framework so that it can accommodate me labels for which we do not have explicit models. This enhanced Level Building algorithm is then coupled with a trigram grammar model to optimally segment and label sign language sentences. We demonstrate the efficiency of the algorithm using a single view video dataset of continuous sign language sentences. We obtain 83% word level recognition rate with the enhanced Level Building approach, as opposed to a 20% recognition rate using a classical Level Building framework on the same dataset. The proposed approach is novel since it does not need explicit models for movement epenthesis.
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