Robust Person-Independent Visual Sign Language Recognition
Robust Person-Independent Visual Sign Language Recognition
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DOI：
10.1007/11492429_63
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发表时间：
2005-06
期刊：
影响因子：
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通讯作者：
Jörg Zieren;K. Kraiss
中科院分区：
文献类型：
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作者：
Jörg Zieren;K. Kraiss
手语识别是计算机视觉中一个具有挑战性的研究领域。重叠、歧义和最小对等常见问题经常发生，需要强大的特征提取和处理算法。我们提出了一个系统，在受控环境中以99.3%的准确率对232个孤立的符号进行依赖人的识别。221个标识的独立识别率达到44.1%。在各种不受控制的室内和室外环境中，使用减少的18个手势词汇，6个手语的平均表现达到87.8%。该系统使用背景模型在像素级上从输入视频中去除静态区域。在跟踪阶段，多个假设并行进行，以处理歧义，并促进错误的回顾性纠正。赢家假设是通过应用人体、手部运动和签名过程的高级知识来发现的。通过模板匹配来解决重叠，利用没有或较少重叠的时间相邻帧。对提取的特征进行归一化，以提高个体独立性和鲁棒性，并使用隐马尔可夫模型进行分类。
Sign language recognition constitutes a challenging field of research in computer vision. Common problems like overlap, ambiguities, and minimal pairs occur frequently and require robust algorithms for feature extraction and processing. We present a system that performs person-dependent recognition of 232 isolated signs with an accuracy of 99.3% in a controlled environment. Person-independent recognition rates reach 44.1% for 221 signs. An average performance of 87.8% is achieved for six signers in various uncontrolled indoor and outdoor environments, using a reduced vocabulary of 18 signs.The system uses a background model to remove static areas from the input video on pixel level. In the tracking stage, multiple hypotheses are pursued in parallel to handle ambiguities and facilitate retrospective correction of errors. A winner hypothesis is found by applying high level knowledge of the human body, hand motion, and the signing process. Overlaps are resolved by template matching, exploiting temporally adjacent frames with no or less overlap. The extracted features are normalized for person-independence and robustness, and classified by Hidden Markov Models.