Falling detection based on deep learning and video classifier

Chen Xu; Jinbo Liu; Yihang Liu; Chonghan Wang

doi:10.1117/12.2641030

13 October 2022 Falling detection based on deep learning and video classifier

Chen Xu, Jinbo Liu, Yihang Liu, Chonghan Wang

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122872K (2022) https://doi.org/10.1117/12.2641030
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Nowadays, falls are a common problem for older adults, which leads to injuries and decreased quality of life. According to the Chinese Center for Disease Control and Prevention, falls have become the leading cause of injuries to people over 65. Therefore, how to detect falls quickly becomes very important. In this paper, we assess the performance of five different video classification algorithms, namely, Efficient Convolutional Network for Online Video Understanding (ECO), C3D network, Temporal Segment Network (TSN), NeXtVLAD, Convolutional two-stream Network. Finally, we find that ECO and TSN algorithms perform better, and their accuracy is around 95%. However, ECO has a shorter running time and a faster response, so the ECO algorithm performs best in detecting falls.

Citation Download Citation

Chen Xu, Jinbo Liu, Yihang Liu, and Chonghan Wang "Falling detection based on deep learning and video classifier", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122872K (13 October 2022); https://doi.org/10.1117/12.2641030

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