Video based fall detection accuracy has been largely improved due to the recent progress on deep convolutional neural networks. However, there still exists some challenges, such as lighting variation, complex background, which degrade the accuracy and generalization ability of these approaches. Meanwhile, large computation cost limits the application of existing fall detection approaches. To alleviate these problems, a video based fall detection approach using human poses is proposed in this paper. First, a lightweight pose estimator extracts 2D poses from video sequences and then 2D poses are lifted to 3D poses. Second, we introduce a robust fall detection network to recognize fall events using estimated 3D poses, which increases respective filed and maintains low computation cost by dilated convolutions. The experimental results show that the proposed fall detection approach achieves a high accuracy of 99.83% on large benchmark action recognition dataset NTU RGB+D and real-time performance of 18 FPS on a non-GPU platform and 63 FPS on a GPU platform.

中文翻译：

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使用人体姿势的基于视频的跌倒检测

由于深度卷积神经网络的最新进展，基于视频的跌倒检测精度已大大提高。然而，仍然存在一些挑战，例如光照变化、复杂的背景，降低了这些方法的准确性和泛化能力。同时，大的计算成本限制了现有跌倒检测方法的应用。为了缓解这些问题，本文提出了一种使用人体姿势的基于视频的跌倒检测方法。首先，轻量级姿势估计器从视频序列中提取 2D 姿势，然后将 2D 姿势提升为 3D 姿势。其次，我们引入了一个强大的跌倒检测网络来使用估计的 3D 姿态识别跌倒事件，这增加了各个领域并通过扩张卷积保持了低计算成本。