Spotting fingerspelled words from sign language video by temporally regularized canonical component analysis
Spotting fingerspelled words from sign language video by temporally regularized canonical component analysis
复制标题
通过时间正则规范成分分析从手语视频中发现手语拼写的单词
DOI：
10.1109/isba.2016.7477238
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
K. Fukui
中科院分区：
文献类型：
--
作者：
Shohei Tanaka;A. Okazaki;N. Kato;H. Hino;K. Fukui
提出了一种手语视频中特定词语的识别方法。在使用日本手语的课程和谈话中，没有定义符号的单词，如人名，物体和地名，由日本手指字母表中的多个字符组成。识别这些单词的困难使得人们强烈要求能够识别特定单词，以帮助口译员和听众理解演讲。我们解决的定位任务，采用时间正则化典型相关分析（TRCCA），它可以同时处理形状和运动信息的3D对象的基本思想。TRCCA的分类精度提高了两个功能：1）多时间尺度的并行处理，2）强隐式特征映射的非线性正交化。增强的TRCCA被称为“核正交TRCCA（KOTRCCA）"。实验证明，该方法使用KOTRCCA的有效性，发现八个不同的话在手语视频。
A method for spotting specific words in sign language video is proposed. In classes and talks given using Japanese Sign Language, words that do not have a defined sign, such as the names of people, objects, and places, are represented by sets of multiple characters from the Japanese finger alphabet. The difficulty of recognizing these words has created strong demand for the ability to spot specific words in order to help interpreters and the audience to follow a talk. We address the spotting task by employing the basic idea of temporal regularized canonical correlation analysis (TRCCA), which can simultaneously handle shape and motion information about a 3D object. The classification accuracy of TRCCA is enhanced by incorporating two functions: 1) parallel processing with multiple time scales, 2) strong implicit feature mapping by nonlinear orthogonalization. The enhanced TRCCA is called "kernel orthogonal TRCCA (KOTRCCA)". The effectiveness of the proposed method using KOTRCCA is demonstrated through experiments spotting eight different words in sign language videos.