• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on machine vision >Laban Movement Analysis to Classify Emotions from Motion
【24h】

Laban Movement Analysis to Classify Emotions from Motion

机译：Laban运动分析可将运动中的情绪分类

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we present the study of Laban Movement Analysis (LMA) to understand basic human emotions from nonverbal human behaviors. While there are a lot of studies on understanding behavioral patterns based on natural language processing and speech processing applications, understanding emotions or behavior from non-verbal human motion is still a very challenging and unexplored field. LMA provides a rich overview of the scope of movement possibilities. These basic elements can be used for generating movement or for describing movement. They provide an inroad to understanding movement and for developing movement efficiency and expressiveness. Each human being combines these movement factors in his/her own unique way and organizes them to create phrases and relationships which reveal personal, artistic, or cultural style. In this work, we build a motion descriptor based on a deep understanding of Laban theory. The proposed descriptor builds up on previous works and encodes experiential features by using temporal windows. We present a more conceptually elaborate formulation of Laban theory and test it in a relatively new domain of behavioral research with applications in human-machine interaction. The recognition of affective human communication may be used to provide developers with a rich source of information for creating systems that are capable of interacting well with humans. We test our algorithm on UCLIC dataset which consists of body motions of 13 non-professional actors portraying angry, fear, happy and sad emotions. We achieve an accuracy of 87.30% on this dataset.
机译：在本文中，我们将对拉班运动分析（LMA）进行研究，以从非语言人类行为中了解人类的基本情感。尽管有很多关于基于自然语言处理和语音处理应用程序来理解行为模式的研究，但是从非语言人类动作中了解情绪或行为仍然是一个非常具有挑战性和探索性的领域。 LMA提供了移动可能性范围的丰富概述。这些基本元素可用于生成运动或描述运动。它们为理解运动以及开发运动效率和表现力提供了帮助。每个人都以自己独特的方式结合了这些运动因素，并组织起来以创造出揭示个人，艺术或文化风格的短语和关系。在这项工作中，我们基于对拉班理论的深刻理解来构建运动描述符。提出的描述符建立在先前的工作基础上，并通过使用时间窗口对体验特征进行编码。我们提出了拉班理论的概念上更详尽的表述，并在行为研究的新领域对其进行了测试，并将其应用于人机交互。情感人类交流的认识可用于为开发人员提供丰富的信息源，以创建能够与人类良好互动的系统。我们在UCLIC数据集上测试了我们的算法，该数据集包含13个非职业演员的身体动作，这些动作描绘了愤怒，恐惧，快乐和悲伤的情绪。在此数据集上，我们达到87.30％的准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号