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Indoor human activity recognition using high-dimensional sensors and deep neural networks

机译：使用高维传感器和深神经网络的室内人类活动识别

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摘要

Many smart home applications rely on indoor human activity recognition. This challenge is currently primarily tackled by employing video camera sensors. However, the use of such sensors is characterized by fundamental technical deficiencies in an indoor environment, often also resulting in a breach of privacy. In contrast, a radar sensor resolves most of these flaws and maintains privacy in particular. In this paper, we investigate a novel approach toward automatic indoor human activity recognition, feeding high-dimensional radar and video camera sensor data into several deep neural networks. Furthermore, we explore the efficacy of sensor fusion to provide a solution in less than ideal circumstances. We validate our approach on two newly constructed and published data sets that consist of 2347 and 1505 samples distributed over six different types of gestures and events, respectively. From our analysis, we can conclude that, when considering a radar sensor, it is optimal to make use of a three-dimensional convolutional neural network that takes as input sequential range-Doppler maps. This model achieves 12.22% and 2.97% error rate on the gestures and the events data set, respectively. A pretrained residual network is employed to deal with the video camera sensor data and obtains 1.67% and 3.00% error rate on the same data sets. We show that there exists a clear benefit in combining both sensors to enable activity recognition in the case of less than ideal circumstances.
机译：许多智能家庭应用依赖室内人类活动识别。目前主要通过采用摄像机传感器来解决这一挑战。然而，这种传感器的使用是在室内环境中的基本技术缺陷的特点，通常也导致违反隐私。相比之下，雷达传感器可以解决大部分缺陷并特别地保持隐私。在本文中，我们研究了一种新颖的室内人类活动识别方法，将高维雷达和摄像机传感器数据馈送到几个深神经网络中。此外，我们探讨了传感器融合的功效，以便在较小的情况下提供解决方案。我们在两个新建和发布的数据集上验证了我们的方法，该数据集分别包括分布在六种不同类型的手势和事件上的2347和1505个样本。从我们的分析中，我们可以得出结论，在考虑雷达传感器时，利用三维卷积神经网络是最佳的，该神经网络作为输入顺序范围 - 多普勒图。该模型分别在手势和事件数据集中实现了12.22％和2.97％的错误率。采用备用残余网络来处理摄像机传感器数据，并在相同的数据集中获得1.67％和3.00％的错误率。我们表明，在结合两个传感器以使活动识别的情况下，存在明显的益处，在较小的情况下实现活动识别。

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