Video Based Fall Detection Using Human Poses
This work addresses fall detection for elderly care or healthcare monitoring, but it is incremental as it builds on existing pose estimation and deep learning techniques.
The paper tackles video-based fall detection by proposing a method using human poses to address challenges like lighting variation and high computation costs, achieving 99.83% accuracy on the NTU RGB+D dataset and real-time performance up to 63 FPS.
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.