Elderly Fall Detection Based on Improved SSD Algorithm

Jiancheng Zou, Na Zhu, Bailin Ge, Don Hong
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Abstract

: We propose an improved a single-shot detector (SSD) algorithm to detect falls of the elderly. The VGG16 network part of the SSD network is replaced with the MobilenetV2 network. At the same time, we change the infrastructure of MobilenetV2 network, the three layers that were not down-sampled at the end were removed, which can make the model structure simpler and faster to detect. The complete Intersection-over-Union (CIoU) loss function is introduced to get a good regression of the target borders that have different sizes and different proportions. We use Feature Pyramid Network (FPN) for up-sampling, it can fuse low-level feature maps with high resolution and high-level feature maps with rich semantic information. For sampling results, we use the Secure Shell (SSH) module to extract different receptive fields, which improves the detection accuracy. Our model ensures that the accuracy of the elderly fall detection remains unchanged, but it greatly improves the detection speed that only takes 10 milliseconds to detect a picture.
基于改进SSD算法的老年人跌倒检测
我们提出了一种改进的单镜头检测器(SSD)算法来检测老年人的跌倒。将SSD网络中的VGG16网络部分替换为MobilenetV2网络。同时,我们改变了MobilenetV2网络的基础结构,去掉了最后没有下采样的三层,使得模型结构更简单,检测速度更快。为了对不同尺寸、不同比例的目标边界进行较好的回归,引入了完全相交-超并(CIoU)损失函数。采用特征金字塔网络(Feature Pyramid Network, FPN)进行上采样,可以融合具有高分辨率的低级特征图和具有丰富语义信息的高级特征图。对于采样结果,我们使用Secure Shell (SSH)模块提取不同的接受域,提高了检测精度。我们的模型在保证老年人跌倒检测精度不变的情况下,大大提高了检测速度,检测一张图片只需要10毫秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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