Ge Chenjie, Gu Irene Yu-Hua, Yang Jie
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1572-1575. doi: 10.1109/EMBC.2018.8512586.
This paper addresses the issue of fall detection from videos for e-healthcare and assisted-living. Instead of using conventional hand-crafted features from videos, we propose a fall detection scheme based on co-saliency-enhanced recurrent convolutional network (RCN) architecture for fall detection from videos. In the proposed scheme, a deep learning method RCN is realized by a set of Convolutional Neural Networks (CNNs) in segment-levels followed by a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), to handle the time-dependent video frames. The co-saliency-based method enhances salient human activity regions hence further improves the deep learning performance. The main contributions of the paper include: (a) propose a recurrent convolutional network (RCN) architecture that is dedicated to the tasks of human fall detection in videos; (b) integrate a co-saliency enhancement to the deep learning scheme for further improving the deep learning performance; (c) extensive empirical tests for performance analysis and evaluation under different network settings and data partitioning. Experiments using the proposed scheme were conducted on an open dataset containing multicamera videos from different view angles, results have shown very good performance (test accuracy 98.96%). Comparisons with two existing methods have provided further support to the proposed scheme.
本文探讨了用于电子医疗保健和辅助生活的视频跌倒检测问题。我们提出了一种基于共显著性增强循环卷积网络（RCN）架构的跌倒检测方案，而不是使用传统的视频手工特征来进行视频跌倒检测。在所提出的方案中，一种深度学习方法RCN由一组分段级别的卷积神经网络（CNN）实现，随后是循环神经网络（RNN）、长短期记忆网络（LSTM），以处理与时间相关的视频帧。基于共显著性的方法增强了显著的人类活动区域，从而进一步提高了深度学习性能。本文的主要贡献包括：（a）提出一种专门用于视频中人体跌倒检测任务的循环卷积网络（RCN）架构；（b）将共显著性增强集成到深度学习方案中，以进一步提高深度学习性能；（c）在不同网络设置和数据划分下进行广泛的实证测试以进行性能分析和评估。使用所提出的方案在一个包含来自不同视角的多摄像头视频的开放数据集上进行了实验，结果显示出非常好的性能（测试准确率98.96%）。与两种现有方法的比较为所提出的方案提供了进一步的支持。
