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Monitoring Indoor Living Spaces using Depth Information

机译：使用深度信息监控室内生活空间

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Longer life expectancy is resulting in a steady increase in population that needs specific services to support their everyday routines. Public and private structures that provide services to these communities exist, however the increasing demands for service place these structures under stress and increased expenses. Assistive living systems can help reduce the demand and cost for these services by supporting the elderly at their homes, improving their quality of life in the process. In this paper we propose a solution that solely uses the depth information from RGB-D cameras to monitor the elderly within indoor living spaces. Deep learning on depth video data is used to detect the elderly and report the position to an application. This position information creates paths over time that can be monitored remotely by family members and caregivers to understand the behavior of the elderly and take appropriate action when needed. Experimental results show that the system manages to detect the person with an accuracy of 66.5% and a tracking accuracy of 59.1%.
机译：较长的预期寿命导致人口稳步增加，需要具体的服务来支持他们的日常生活。存在为这些社区提供服务的公共和私人结构，但服务需求的日益增长的需求将这些结构置于压力和增加的费用下。辅助生活系统可以通过在家庭中支持老年人来帮助降低这些服务的需求和成本，从而提高他们在过程中的生活质量。在本文中，我们提出了一种单独使用来自RGB-D相机的深度信息的解决方案，以监测室内生活空间内的老年人。深度视频数据的深度学习用于检测老人并将位置报告给应用程序。此职位信息会在随时间创建路径，这些路径可以通过家庭成员和护理人员远程监视，以了解老人的行为并在需要时采取适当的行动。实验结果表明，该系统设法检测精度为66.5％的人，跟踪精度为59.1％。

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