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首页> 外文会议>Proceedings of the 2016 IEEE International Conference on Wireless Communications, Signal Processing and Networking >Continuous sign language recognition from tracking and shape features using Fuzzy Inference Engine
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Continuous sign language recognition from tracking and shape features using Fuzzy Inference Engine

机译：使用模糊推理引擎从跟踪和形状特征中连续识别手语

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摘要

Fuzzy classifying continuous sign language videos with simple backgrounds with tracking and shape combined features is the focus of this work. Tracking and capturing hand position vectors is the artwork of horn schunck optical flow algorithm. Active contours extract shape features from sign frames in the video sequence. The two most dominant features of sign language are combined to build sign features. This feature matrix is the training vector for Fuzzy Inference Engine (FIS). The classifier is tested with 50 signs in a video sequence. Ten different signers created 50 signs. Different instances of FIS are tested with different combination of feature vectors. The results are compared with our previous work using no tracking and with discrete sign language database. A word matching score (WMS) gauges the performance of the classifiers. A 92.5% average matching score is reported in this work. A through comparisons for FIS gesture classifier between Discrete Cosine Transform features, Elliptical Fourier descriptor features and the proposed hybrid features for continuous sign language videos show a 40% jump in word matching score.
机译：本工作的重点是对具有简单背景，跟踪和形状组合功能的连续手语视频进行模糊分类。跟踪和捕获手的位置向量是horn schunck光流算法的作品。活动轮廓从视频序列的符号帧中提取形状特征。手语的两个最主要的特征被组合以构建手语特征。该特征矩阵是模糊推理引擎（FIS）的训练向量。在视频序列中使用50个符号测试分类器。十个不同的签名者创建了50个签名。使用特征向量的不同组合测试FIS的不同实例。将结果与我们以前的工作（不使用跟踪和离散手语数据库）进行了比较。单词匹配分数（WMS）衡量分类器的性能。据报道这项工作的平均匹配分数为92.5％。离散余弦变换特征，椭圆傅立叶描述符特征和为连续手语视频提出的混合特征之间的FIS手势分类器的完全比较显示，单词匹配分数提高了40％。

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