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首页> 外文会议>2014 International Conference on Issues and Challenges in Intelligent Computing Techniques >A framework for recognizing and segmenting sign language gestures from continuous video sequence using boosted learning algorithm
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A framework for recognizing and segmenting sign language gestures from continuous video sequence using boosted learning algorithm

机译：使用增强学习算法从连续视频序列中识别和分割手语手势的框架

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摘要

The problem of vision-based sign language recognition, which is used to translate signs to English sentence, is addressed in this paper. A fully automatic system to recognize signs that starts with breaking up signs into manageable subunits is proposed. A framework for segmenting and tracking skin objects from signing videos is described. A boosting algorithm to learn a subset of weak classifiers for extracted features to combine them into a strong classifier for each sign is then applied. A joint learning strategy to share subunits across sign classes is adopted, which leads to a more efficient classification of sign gestures. Experimental results shown by the system demonstrate that the proposed approach is promising to build an effective and scalable system on real-world hand gesture recognition from continuous video sequences.
机译：本文解决了基于视觉的手语识别问题，该手势用于将手语翻译成英语句子。提出了一种识别符号的全自动系统，该系统首先将符号分解为可管理的子单元。描述了一种用于从签名视频中分割和跟踪皮肤对象的框架。然后应用一种增强算法来学习弱分类器的子集以提取特征，以针对每个符号将它们组合为强分类器。采用了一种联合学习策略，可以在各个符号类之间共享子单元，从而可以更有效地对符号手势进行分类。该系统显示的实验结果表明，该方法有望在连续视频序列的真实手势识别上构建有效且可扩展的系统。

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