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PreCount: a predictive model for correcting real-time occupancy count data

机译：PreCount：用于校正实时占用计数数据的预测模型

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Sensing the number of people occupying a building in real-time facilitates a number of pervasive applications within the area of building energy optimization and adaptive control. To ascertain occupant counts, the adoption of camera-based sensors i.e. 3D stereo-vision and thermal cameras have grown significantly. However, camera-based sensors can only produce occupant counts with accumulating errors. Existing methods for correcting such errors can only correct erroneous count data at the end of the day and not in real-time. However, many applications depend on real-time corrected counts. In this paper, we present an algorithm named PreCount for accurately correcting raw counts in real-time. The core idea of PreCount is to learn error estimates from the past. We evaluated the accuracy of the PreCount algorithm using datasets from four buildings. Also, the Normalized Root Mean Squared Error was used to evaluate the performance of PreCount. Our evaluation results show that in real-time PreCount achieved a significantly lower Normalized Root Mean Squared Error compared to raw counts and other correction approach with a maximum error reduction of 68% when benchmarked with ground truth data. By presenting a more accurate algorithm for estimating occupant counts in real-time, we hope to enable buildings to better serve the actual number of people to improve both occupant comfort and energy efficiency.
机译：实时感知占用建筑物的人数有助于建筑物能源优化和自适应控制领域中的许多普遍应用。为了确定人员数量，基于摄像头的传感器（即3D立体视觉和热像仪）的采用已显着增长。但是，基于摄像头的传感器只能产生带有累积误差的乘员计数。现有的纠正此类错误的方法只能在一天结束时纠正错误的计数数据，而不能实时纠正。但是，许多应用程序都依赖实时校正计数。在本文中，我们提出了一种名为PreCount的算法，可以实时准确地校正原始计数。 PreCount的核心思想是学习过去的错误估计。我们使用来自四座建筑物的数据集评估了PreCount算法的准确性。同样，使用归一化均方根误差来评估PreCount的性能。我们的评估结果表明，与原始计数和其他校正方法相比，实时PreCount可以显着降低归一化均方根误差，而以地面真实数据为基准时，最大误差减少了68％。通过提出一种更准确的实时估算人员数量的算法，我们希望使建筑物能够更好地服务于实际人数，从而改善人员舒适度和能源效率。

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