Real-Time Human Body Posture Estimation Using Neural Networks
Real-Time Human Body Posture Estimation Using Neural Networks
复制标题
使用神经网络进行实时人体姿势估计
DOI：
10.1299/jsmec.44.618
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
T. Uemura
中科院分区：
文献类型：
--
作者：
Kazuhiko Takahashi;T. Uemura
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相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是轮廓上特征点的位置。决策逻辑单元综合每个人工神经网络的输出向量，然后计算人体特征点的二维坐标。该方法在个人计算机上实现，实时运行（17-20帧/秒）。实验结果验证了该方法用于人体姿态估计的可行性和有效性。将所提出的估计方法应用于立体视觉系统，还可以实现实时的三维人体姿态估计。
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. By applying the proposed estimation method to a stereo vision system, real-time 3D human body posture estimation can also be achieved.