论文标题
使用手接跟随点分析的美国手语识别
American Sign Language Identification Using Hand Trackpoint Analysis
论文作者
论文摘要
手语帮助有效的说话和听力障碍的人有效地与他人交流。手语标识是计算机视觉领域的一个挑战领域，尽管尚未解决一些挑战，但最近的发展已经能够为任务取得几乎完美的结果。在本文中，我们提出了一种基于机器学习的新型管道，该管道使用手轨迹点来识别美国手语的标识。我们将手势转换为一系列的手跟踪点坐标，这些坐标是我们系统的输入。为了提高解决方案的效率，我们尝试了28种不同的预处理技术组合，每种都在三种不同的机器学习算法上运行，即k-nearest邻居，随机森林和神经网络。它们的性能形成对比，以确定最佳的预处理方案和算法对。我们的系统获得了95.66％的准确性，以识别美国手语手势。
Sign Language helps people with Speaking and Hearing Disabilities communicate with others efficiently. Sign Language identification is a challenging area in the field of computer vision and recent developments have been able to achieve near perfect results for the task, though some challenges are yet to be solved. In this paper we propose a novel machine learning based pipeline for American Sign Language identification using hand track points. We convert a hand gesture into a series of hand track point coordinates that serve as an input to our system. In order to make the solution more efficient, we experimented with 28 different combinations of pre-processing techniques, each run on three different machine learning algorithms namely k-Nearest Neighbours, Random Forests and a Neural Network. Their performance was contrasted to determine the best pre-processing scheme and algorithm pair. Our system achieved an Accuracy of 95.66% to identify American sign language gestures.
