The automatic detection of falls within environments where sensors are deployed has attracted considerable research interest due to the prevalence and impact of falling people, especially the elderly. In this work, we analyze the capabilities of non-invasive thermal vision sensors to detect falls using several architectures of convolutional neural networks. First, we integrate two thermal vision sensors with different capabilities: (1) low resolution with a wide viewing angle and (2) high resolution with a central viewing angle. Second, we include fuzzy representation of thermal information. Third, we enable the generation of a large data set from a set of few images using ad hoc data augmentation, which increases the original data set size, generating new synthetic images. Fourth, we define three types of convolutional neural networks which are adapted for each thermal vision sensor in order to evaluate the impact of the architecture on fall detection performance. The results show encouraging performance in single-occupancy contexts. In multiple occupancy, the low-resolution thermal vision sensor with a wide viewing angle obtains better performance and reduction of learning time, in comparison with the high-resolution thermal vision sensors with a central viewing angle.

中文翻译：

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卷积神经网络对异构热视觉传感器跌倒分类的评估

由于人们，尤其是老年人跌倒的普遍性和影响，在部署了传感器的环境中自动检测跌倒引起了相当大的研究兴趣。在这项工作中，我们分析了非侵入式热视觉传感器使用卷积神经网络的几种架构检测跌倒的能力。首先，我们集成了两个具有不同功能的热视觉传感器：（1）具有宽视角的低分辨率和（2）具有中心视角的高分辨率。其次，我们包括热信息的模糊表示。第三，我们使用临时数据增强从一组少量图像生成大数据集，这增加了原始数据集的大小，生成新的合成图像。第四，我们定义了三种类型的卷积神经网络，它们适用于每个热视觉传感器，以评估架构对跌倒检测性能的影响。结果显示在单人入住环境中表现令人鼓舞。在多人入住时，与具有中心视角的高分辨率热视觉传感器相比，具有宽视角的低分辨率热视觉传感器获得了更好的性能并减少了学习时间。