A Two-Stream Approach to Fall Detection With MobileVGG
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TLDR
The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory, therefore, it is suitable for mobile devices.Abstract:
The existing deep learning methods for human fall detection have difficulties to distinguish falls from similar daily activities such as lying down because of not using the 3D network. Meanwhile, they are not suitable for mobile devices because they are heavyweight methods and consume a large number of memories. In order to alleviate these problems, a two-stream approach to fall detection with the MobileVGG is proposed in this paper. One stream is based on the motion characteristics of the human body for detection of falls, while the other is an improved lightweight VGG network, named the MobileVGG, put forward in the paper. The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution, depth convolution and point convolution. The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model. The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory. Therefore, it is suitable for mobile devices.read more
Citations
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Elderly Fall Detection Systems: A Literature Survey
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Vibration sensing-based human and infrastructure safety/health monitoring: A survey
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Journal ArticleDOI
Fall detection using body geometry and human pose estimation in video sequences
TL;DR: In this paper , a fall detection approach that explores human body geometry available at different frames of the video sequence is presented, especially, pose estimation, the angle and the distance between the vector formed by the identified facial image and the center hip of the body, and the vector aligned with the horizontal axis of the centre hip, are employed to construct new distinctive image features.
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Optimal Training Configurations of a CNN-LSTM-Based Tracker for a Fall Frame Detection System.
TL;DR: In this paper, an automated fall frame detection system, which is referred to as SmartConvFall, is proposed to detect the exact fall frame in a video sequence, based on the assumption that a large movement difference with respect to the ground level along the vertical axis can be observed if a fall incident happened.
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