DTW Based Clustering to Improve Hand Gesture Recognition
DTW Based Clustering to Improve Hand Gesture Recognition
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DOI：
10.1007/978-3-642-25446-8_8
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
Cem Keskin;A. Cemgil;L. Akarun
中科院分区：
文献类型：
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作者：
Cem Keskin;A. Cemgil;L. Akarun
基于视觉的手势识别系统跟踪手势，提取手势的空间轨迹和形状信息，然后用机器学习方法对其进行分类。在这项工作中，我们提出了一种基于动态时间规整(DTW)的预聚类技术，以显著提高人机交互(HCI)文献中使用的各种图形模型的手势识别精度。利用12个人在空中书写的10个数字组成的1200个样本的数据集，验证了该方法的有效性。隐马尔可夫模型(HMM)、输入输出隐马尔可夫模型(IOHMM)、隐条件随机场(HCRF)和隐式半马尔可夫模型(HsMM)的一种显式持续时间模型(EDM)分别在原始数据集和聚类数据集上进行训练。比较了两种情况下各模型的最优模型复杂度和识别精度。实验表明，该方法的识别率有了很大的提高，对大多数模型都达到了完美的识别精度，最优模型的复杂度显著降低。
Vision based hand gesture recognition systems track the hands and extract their spatial trajectory and shape information, which are then classified with machine learning methods. In this work, we propose a dynamic time warping (DTW) based pre-clustering technique to significantly improve hand gesture recognition accuracy of various graphical models used in the human computer interaction (HCI) literature. A dataset of 1200 samples consisting of the ten digits written in the air by 12 people is used to show the efficiency of the method. Hidden Markov model (HMM), input-output HMM (IOHMM), hidden conditional random field (HCRF) and explicit duration model (EDM), which is a type of hidden semi Markov model (HSMM) are trained on the raw dataset and the clustered dataset. Optimal model complexities and recognition accuracies of each model for both cases are compared. Experiments show that the recognition rates undergo substantial improvement, reaching perfect accuracy for most of the models, and the optimal model complexities are significantly reduced.