Along with methods such as Hidden Markov Model and Linear Discriminant Analysis, environmental sensors including temperature, humidity or carbon dioxide are used to estimate indoor occupancy, which have various applications. Most of the previous studies have neglected the time-dependent character of the indoor occupancy information or real-time response of the system. In this paper, we propose to use low invasive, fast-sampling infrared array sensors to collect data from the actual scene and establish the Inhomogeneous Hidden Markov Model to capture the time dependence of occupancy for buildings occupancy estimation. First, to avoid raw sensor datas susceptibility to external influences, the non-negative matrix factorization is adopted to reduce the dimension of the raw matrix and eliminate interferences caused by environmental changes. Second, the Softmax Regression Model is used to calculate the emission probability matrix for clarifying the dynamic relationship between environmental parameters and indoor occupancy, which is weak. Third, the Forward algorithm and the Viterbi algorithm are applied to achieve online and off-line estimation. Experiments are made with real recorded data. Our performance results demonstrate that method we proposed is effective for indoor occupancy estimation.

中文翻译：

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基于红外阵列传感器检测的建筑物占用估计

连同隐马尔可夫模型和线性判别分析等方法，包括温度、湿度或二氧化碳在内的环境传感器用于估计室内占用率，具有多种应用。以往的研究大多忽略了室内占用信息或系统实时响应的时间相关性。在本文中，我们建议使用低侵入性、快速采样的红外阵列传感器从实际场景中收集数据，并建立非齐次隐马尔可夫模型来捕获建筑物占用率估计的占用率的时间依赖性。首先，为了避免原始传感器数据对外部影响的敏感性，采用非负矩阵分解来降低原始矩阵的维数，消除环境变化带来的干扰。第二，Softmax 回归模型用于计算排放概率矩阵，以阐明环境参数与室内占用率之间的动态关系，该矩阵较弱。第三，应用Forward算法和Viterbi算法实现在线和离线估计。实验是用真实记录的数据进行的。我们的性能结果表明我们提出的方法对于室内占用估计是有效的。