Solving the challenge of occupancy prediction is crucial in order to design efficient and sustainable office spaces and automate lighting, heating, and air circulation in these facilities. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects, which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors; however, they have not addressed nor compensated for such heat artifacts. Therefore, in this paper, we presented a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We used a low-resolution $(8\times 8)$ and non-intrusive heat sensor to collect data from an actual meeting room. We proposed two novel workflows to predict the occupancy; one that is based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we used several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyzed noise resources that affect the heat sensor data. The experiments show that the feature classification based method gives high accuracy when the data are clean from noise artifacts. However, when there are noise artifacts, the computer vision based method can compensate for those artifacts providing robust results. Because the computer vision based method requires an empty room recording, the feature classification based method should be chosen either when there is no expectancy of seeing noise artifacts in the data or when there is no empty recording available. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

中文翻译：

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使用低成本和低分辨率热传感器进行智能办公室的占用预测


解决入住率预测的挑战对于设计高效且可持续的办公空间以及这些设施中的照明、供暖和空气循环自动化至关重要。在需要观察大面积的办公空间，必须使用多个传感器进行全覆盖。在这些情况下，保持较低的成本通常很重要，但也要确保使用此类环境的人的隐私得到保护。低成本和低分辨率的热（热）传感器对于构建解决这些问题的解决方案非常有用。然而，它们对噪声伪影极其敏感，这些伪影可能是由离开该空间的人的热印迹或其他正在使用电力或暴露在阳光下的物体引起的。有一些早期的占用预测解决方案采用低分辨率热传感器；然而，他们没有解决或补偿这种热伪影。因此，在本文中，我们提出了一种低成本、低能耗的智能空间实现，根据人们的活动是静态还是动态来预测环境中的人数。我们使用了低分辨率$（8\times 8）$和非侵入式热传感器，用于从实际会议室收集数据。我们提出了两种新颖的工作流程来预测占用率；一种基于计算机视觉，一种基于机器学习。 除了比较这些不同工作流程的优缺点之外，我们还使用了几种最先进的可解释性方法，以便对算法参数以及图像属性如何影响最终性能进行详细分析。此外，我们还分析了影响热传感器数据的噪声资源。实验表明，当数据不含噪声伪影时，基于特征分类的方法具有较高的准确性。然而，当存在噪声伪影时，基于计算机视觉的方法可以补偿这些伪影，从而提供稳健的结果。由于基于计算机视觉的方法需要空房间记录，因此当预计不会在数据中看到噪声伪影或没有可用的空记录时，应选择基于特征分类的方法。我们希望我们的分析有助于理解如何在这些环境中处理极低分辨率的热图像。所提出的工作流程可用于智能办公室以外的各种领域和应用程序，其中占用率预测至关重要，例如对于老年人护理。