Most existing deep-learning-based fall detection methods use either 2D neural network without considering movement representation sequences, or whole sequences instead of only those in the fall period. These characteristics result in inaccurate extraction of human action features and failure to detect falls due to background interferences or activity representation beyond the fall period. To alleviate these problems, a skeleton-based 3D consecutive-low-pooling neural network (S3D-CNN) for fall detection is proposed in this paper. In the S3D-CNN, an activity feature clustering selector is designed to extract the skeleton representation in depth videos using pose estimation algorithm and form optimized skeleton sequence of fall period. A 3D consecutive-low-pooling (3D-CLP) neural network is proposed to process these representation sequences by improving network in terms of layer number, pooling kernel size, and single input frame number. The proposed method is evaluated on public and self-collected datasets respectively, outperforming the existing methods.

大多数现有的基于深度学习的跌倒检测方法都使用2D神经网络而不考虑运动表示序列，或者使用整个序列而不是仅在跌落期间的那些。这些特征导致人为动作特征的提取不准确，并且由于背景干扰或超出跌倒时间的活动表示而无法检测到跌倒。为了缓解这些问题，本文提出了一种用于跌倒检测的基于骨架的3D连续低池神经网络（S3D-CNN）。在S3D-CNN中，活动特征聚类选择器被设计为使用姿态估计算法提取深度视频中的骨骼表示，并形成优化的下降周期骨骼序列。提出了一种3D连续低池（3D-CLP）神经网络，通过在层数，池内核大小和单个输入帧数方面改进网络来处理这些表示序列。所提出的方法分别在公共数据集和自我收集的数据集上进行了评估，优于现有方法。