激光与光电子学进展, 2020, 57 (16): 161024, 网络出版: 2020-08-05
基于卷积神经网络和XGBoost的摔倒检测 下载: 1055次
Fall Detection Based on Convolutional Neural Network and XGBoost
图像处理 卷积神经网络 squeeze-and-excitation模块 YOLO-v3 XGBoost 摔倒检测 image processing convolutional neural network squeeze-and-excitation block YOLO-v3 XGBoost fall detection
摘要
提出了一种基于卷积神经网络和XGBoost的摔倒检测算法。采用基于squeeze-and-excitation模块的YOLO-v3算法对图片进行人体区域检测,在此基础上使用人体姿态估计网络获取人体关节点并提取出特征向量,再将其输入XGBoost进行训练,进而判断人体是否摔倒。实验结果表明,所提出的摔倒检测算法准确率较高,达到98.3%。
Abstract
This paper proposes a fall detection algorithm based on convolutional neural network and XGBoost. The YOLO-v3 algorithm based on the squeeze-and-excitation block is used to detect the human body area of the picture. Then, the human body pose estimation network is used to obtain the human body joints and feature vectors. Finally, we input the feature vectors into the XGBoost for training to determine whether the human body falls. The experimental results show that the proposed fall detection algorithm has a high accuracy of 98.3%.
赵心驰, 胡岸明, 何为. 基于卷积神经网络和XGBoost的摔倒检测[J]. 激光与光电子学进展, 2020, 57(16): 161024. Xinchi Zhao, Anming Hu, Wei He. Fall Detection Based on Convolutional Neural Network and XGBoost[J]. Laser & Optoelectronics Progress, 2020, 57(16): 161024.