基于改进YOLOv5s的老人跌倒识别算法研究
Journal of Chongqing University of Science and Technology(Natural Sciences Edition)(2023)
Abstract
为应对人口老龄化加剧带来的老年人居家安全问题,提出了一种实时准确的跌倒识别算法.目前基于计算机视觉的跌倒检测主要存在跌倒类间相似性大、监控角度相对固定而跌倒角度多变、特征提取困难、难以联系图像依赖关系等问题.为此,采用同一动作8个拍摄角度的数据集,使模型能够充分学习到多种特征;将所有跌倒行为归为一类,总类别数为二分类;改进原网络的候选框、添加CC-Net、改进非极大值抑制算法,进行分类训练,并对训练好的模型进行对比评估.实验结果表明,该方法的平均准确率比YOLOv4和YOLOv5s模型分别提高了 5.9%和2.6%,比SSD算法提高了11.1%,能够基本满足检测需求.
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Key words
fall detection,fall posture,similarity between classes,CC-Net,real-time detection
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