@article{ref1,
title="Robust fall detection in video surveillance based on weakly supervised learning",
journal="Neural networks",
year="2023",
author="Wu, Lian and Huang, Chao and Zhao, Shuping and Li, Jinkai and Zhao, Jianchuan and Cui, Zhongwei and Yu, Zhen and Xu, Yong and Zhang, Min",
volume="163",
number="",
pages="286-297",
abstract="Fall event detection has been a research hotspot in recent years in the fields of medicine and health. Currently, vision-based fall detection methods have been considered the most promising methods due to their advantages of a non-contact characteristic and easy deployment. However, the existing vision-based fall detection methods mainly use supervised learning in model training and require much time and energy for data annotations. To address these limitations, this work proposes a detection method that uses a weakly supervised learning-based dual-modal network. The proposed method adopts a deep multiple instance learning framework to learn the fall events using weak labels. As a result, the proposed method does not require time-consuming fine-grained annotations. The final detection result of each video is obtained by integrating the information obtained from two streams of the dual-modal network using the proposed dual-modal fusion strategy. Experimental results on two public benchmark datasets and a proposed dataset demonstrate the superiority of the proposed method over the current state-of-the-art methods.<p /> <p>Language: en</p>",
language="en",
issn="0893-6080",
doi="10.1016/j.neunet.2023.03.042",
url="http://dx.doi.org/10.1016/j.neunet.2023.03.042"
}