Neural-network-based real-time human body posture estimation
Neural-network-based real-time human body posture estimation
复制标题
基于神经网络的实时人体姿势估计
DOI：
10.1109/nnsp.2000.890123
复制
发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Jun Ohya
中科院分区：
文献类型：
--
作者：
Kazuhiko Takahashi;T. Uemura;Jun Ohya
提出了一种基于人工神经网络的实时人体姿态估计方法。该网络由三个人工神经网络和一个决策逻辑单元组成。人工神经网络的输入是对从摄像机图像中提取的人体轮廓进行函数分析的结果，人工神经网络的输出指示轮廓上的特征点的位置。决策逻辑单元合成每个ANN的输出向量，然后计算人体特征点的2D坐标。所提出的方法是在个人计算机上实现的，并在实时（17-20帧/秒）运行。实验结果验证了该方法的可行性和有效性。
This paper proposes a real-time human body posture estimation method using ANNs. The network is composed of three ANNs and a decision logic unit. The ANNs' input is the result of a function analysis on a human silhouette's contour extracted from camera images and the ANNs' output indicates the feature points' positions on the contour. The decision logic unit synthesizes each of the ANNs' output vectors and then the 2D coordinates of the human body's feature points are calculated. The proposed method is implemented on a personal computer and runs in real-time (17-20 frames/sec). Experimental results confirm both the feasibility and the effectiveness of the proposed method for estimating human body postures.