计算机工程与科学2011，Vol.33Issue(5)：74-79,6.DOI:10.3969/j.issn.1007-130X.2011.05.015
粒子群优化神经网络在动态手势识别中的应用
Application of the BP Neural Network Based on PSO in Dynamic Gesture Recognition
摘要
Abstract
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/BP神经网络/动态手势识别/粒子群Key words
machine vision/ BP neural network/ dynamic gesture recognition/ particle swarm optimization分类
信息技术与安全科学引用本文复制引用
李文生,姚琼,邓春健..粒子群优化神经网络在动态手势识别中的应用[J].计算机工程与科学,2011,33(5):74-79,6.基金项目
广东省自然科学基金资助项目(8152840301000009) （8152840301000009）
广东省科技计划资助项目(200913030803031) （200913030803031）
