Semantic matchmaking as a way for attitude discovery

M. Ruta, F. Scioscia, S. Ieva, Giovanna Capurso, E. Sciascio
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引用次数: 1

Abstract

Powerful data analysis techniques are currently applied to 3D motion sensing devices like Microsft Kinect for posture and gesture recognition. Though effective, they are computationally intensive and require complex training. This paper proposes an approach for on-the-fly automated posture and gesture recognition, exploiting Kinect and treating the detection as a semantic-based resource discovery problem. A proper data model and an ontology support the annotation of body postures and gestures. The proposed system automatically annotates Kinect data with a Semantic Web standard logic formalism and then attempts to recognize postures by applying a semantic-based matchmaking between descriptions and reference body poses stored in a Knowledge Base. In addition, sequences of postures are compared in order to recognize gestures. The paper presents details about the prototype implementing the framework as well as an early experimental evaluation on a public dataset, in order to assess the feasibility of both ideas and algorithms.
语义配对作为态度发现的一种方式
强大的数据分析技术目前应用于3D运动传感设备,如微软Kinect,用于姿势和手势识别。虽然有效,但它们的计算量很大,需要复杂的训练。本文提出了一种基于Kinect的动态自动姿态和手势识别方法,并将检测视为基于语义的资源发现问题。适当的数据模型和本体支持对身体姿势和手势的注释。该系统采用语义网标准逻辑形式对Kinect数据进行自动注释,然后通过在知识库中存储的描述和参考身体姿势之间应用基于语义的匹配来尝试识别姿势。此外,为了识别手势,还比较了姿势序列。本文详细介绍了实现该框架的原型以及在公共数据集上的早期实验评估,以评估思想和算法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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