Enhanced 3D Residual Network for Human Fall Detection in Video Surveillance

• Suyuan Li,
• Xin Song,
• Jing Cao,
• Siyang Xu,

Vol. 16, No. 12, December 31, 2022
10.3837/tiis.2022.12.012, Download Paper (Free):

• video surveillance
• Fall Detection
• Deep Learning
• residual network
• 3D CNN

Abstract

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential. With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread. However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks. Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers. As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues. In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy. Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network. According to the experimental results, the proposed method outperformed several state-of-the-art methods.

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