文献类型：期刊文献

中文题名：基于单目视觉与深度神经网络的行为识别研究

英文题名：Study on Human Behavior Recognition Based on Monocular Vision and Deep Neural Network

作者：刘兰淇[1];刘钟涛[1]

第一作者：刘兰淇

机构：[1]河南财经政法大学现代教育技术中心,河南郑州450046

第一机构：河南财经政法大学

年份：2022

卷号：52

期号：11

起止页码：2072-2080

中文期刊名：无线电工程

外文期刊名：Radio Engineering

收录：北大核心:【北大核心2020】；

基金：河南科技攻关项目(18210221022)。

语种：中文

中文关键词：单目视觉;静态影像;深度神经网络;卷积神经网络;行为检测;目标检测

外文关键词：monocular vision;still image;deep neural networks;convolutional neural network;behavior detection;target detection

摘要：为解决多人场景下单目视觉行为检测难度大的问题,通过将单目视觉提取的多种特征集融合提出了一种新的行为检测模型。该模型通过深度卷积神经网络(Convolutional Neural Network,CNN)提取区域候选集,经过池化层获得单目视觉的感兴趣目标集;搜索决定人体姿势的25个骨架关键点,提取各个点的位置坐标向量。将2种特征融合传入CNN预测单目视觉的行为标签。实验结果表明,所提模型获得了较高的单目视觉行为检测精度,对多人场景的检测性能也优于其他对比模型。
To resolve the problem of behavior detection difficulty of monocular vision in the multi-person scene,multiple feature sets extracted from monocular vision images are fused to propose a new behavior detection model.Firstly,the region candidates are extracted through deep Convolutional Neural Network(CNN),and the regions of interest of monocular vision images are generated through the pooling layer;then,twenty-five skeleton critical points are located,the body pose is determined by the twenty-five points,and the position coordinate vectors of each point are extracted.Finally,both feature sets are fused,and then the results are transmitted to CNN to predict the behavior labels of monocular vision.The experimental results show that the proposed model achieves a high behavior detection precision of monocular vision still images;the detection performance is better than the other models in multi-person scenes.