Adaptive Gesture Recognition with Variation Estimation for Interactive Systems
Adaptive Gesture Recognition with Variation Estimation for Interactive Systems
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DOI：
10.1145/2643204
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发表时间：
2015-01-01
影响因子：
3.4
通讯作者：
Bevilacqua, Frederic
中科院分区：
文献类型：
--
作者：
Caramiaux, Baptiste;Montecchio, Nicola;Bevilacqua, Frederic
本文提出了一个手势识别/适应系统的人机交互应用程序，超越活动分类，作为手势标记的补充，其特点是运动执行。我们描述了一个基于模板的识别方法，同时对齐输入的手势模板使用顺序蒙特卡罗推理技术。与基于动态规划的标准的基于模板的方法（诸如动态时间规整）相反，该算法具有跟踪真实的时间中的手势变化的自适应过程。该方法在手势执行期间不断更新估计参数和识别结果，这为连续的人机交互提供了关键优势。该技术在几个不同的方式进行评估：识别和早期识别的2D手写笔手势进行评估;适应评估的合成数据;和早期识别和适应评估的用户研究，涉及3D自由空间手势。该方法对噪声具有较强的鲁棒性，并成功地适应了参数变化。此外，它执行识别以及或优于非适应离线基于模板的方法。
This article presents a gesture recognition/adaptation system for human-computer interaction applications that goes beyond activity classification and that, as a complement to gesture labeling, characterizes the movement execution. We describe a template-based recognition method that simultaneously aligns the input gesture to the templates using a Sequential Monte Carlo inference technique. Contrary to standard template-based methods based on dynamic programming, such as Dynamic Time Warping, the algorithm has an adaptation process that tracks gesture variation in real time. The method continuously updates, during execution of the gesture, the estimated parameters and recognition results, which offers key advantages for continuous human-machine interaction. The technique is evaluated in several different ways: Recognition and early recognition are evaluated on 2D onscreen pen gestures; adaptation is assessed on synthetic data; and both early recognition and adaptation are evaluated in a user study involving 3D free-space gestures. The method is robust to noise, and successfully adapts to parameter variation. Moreover, it performs recognition as well as or better than nonadapting offline template-based methods.