作　　者： ; ; ;

机构地区： 中山学院

出　　处： 《计算机工程与科学》 2011年第5期74-79,共6页

摘　　要： 为了提高动态手势学习训练速度和识别准确率,本文提出一种基于粒子群优化BP神经网络的动态手势识别方法。首先基于自然人机交互需要,定义一套基于机器视觉的动态手势模型;在获取指尖运动轨迹的基础上,提取动态手势的特征向量作为神经网络的输入;利用改进的PSO算法训练BP神经网络,得到神经网络的权值和阈值;最后利用训练过的神经网络识别基于机器视觉的动态手势。测试结果表明:改进的PSO算法能够提高神经网络训练速度和精度,进而提高动态手势识别准确率。 In order to improve the training speed and identification accuracy of dynamic gesture,a method of gesture recognition based on the particle swarm optimization（PSO） BP neural network is put forward.First,a set of dynamic gestures is defined for Human-Machine Interaction（HMI）.The engenvectors vectors of dynamic gestures are extracted as the input of the BP neural network on the basis of obtaining the trajectories of moving fingertips.An improved PSO algorithm is used to train the BP neural network and get the weights/thresholds of the network.Finally,the gestures based on machine vision are recognized through the trained BP neural network.The experimental results show that the proposed PSO algorithm can enhance the speed and precision of network training,and improve the accuracy of dynamic gesture recognition.

关 键 词： 机器视觉 神经网络 动态手势识别 粒子群

领　　域： [自动化与计算机技术] [自动化与计算机技术]