...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Pervasive and Mobile Computing >Object relevance weight pattern mining for activity recognition and segmentation
【24h】

Object relevance weight pattern mining for activity recognition and segmentation

机译：用于活动识别和分割的对象相关权重模式挖掘

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Monitoring daily activities of a person has many potential benefits in pervasive computing. These include providing proactive support for the elderly and monitoring anomalous behaviors. A typical approach in existing research on activity detection is to construct sequence-based models of low-level activity features based on the order of object usage. However, these models have poor accuracy, require many parameters to estimate, and demand excessive computational effort. Many other supervised learning approaches have been proposed but they all suffer from poor scalability due to the manual labeling involved in the training process. In this paper, we simplify the activity modeling process by relying on the relevance weights of objects as the basis of activity discrimination rather than on sequence information. For each activity, we mine the web to extract the most relevant objects according to their normalized usage frequency. We develop a KeyExtract algorithm for activity recognition and two algorithms, MaxGap and MaxCain, for activity segmentation with linear time complexities. Simulation results indicate that our proposed algorithms achieve high accuracy in the presence of different noise levels indicating their good potential in real-world deployment.
机译：在普适计算中，监视一个人的日常活动有许多潜在的好处。这些措施包括为老年人提供积极支持，并监测异常行为。现有的活动检测研究中的一种典型方法是根据对象使用的顺序构建基于序列的低级活动特征模型。但是，这些模型的准确性很差，需要估计许多参数，并且需要大量的计算工作。已经提出了许多其他有监督的学习方法，但是由于培训过程中涉及手动标记，它们都具有较差的可伸缩性。在本文中，我们通过依赖对象的相关权重作为活动判别的基础而不是序列信息来简化活动建模过程。对于每个活动，我们都会根据其归一化的使用频率挖掘Web来提取最相关的对象。我们开发了用于活动识别的KeyExtract算法和用于线性时间复杂度的活动分割的两种算法MaxGap和MaxCain。仿真结果表明，我们提出的算法在存在不同噪声水平的情况下实现了较高的精度，表明它们在实际部署中具有良好的潜力。

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