基于手势识别的机器人人机交互技术研究

Research on Human-Robot Interaction Technique Based on Hand Gesture Recognition

研究了基于视觉的动态手势识别技术,采用基于肤色的高斯模型与改进的光流场跟踪算法结合的方法,实现了复杂背景下实时的手势跟踪,具有快速和准确的特点,且具有较好的鲁棒性。对于动态手势识别器,采用了隐马尔可夫模型(HMM)作为训练识别算法。考虑到动态手势特征本身的一些特点,对HMM参数优化算法重估式加以修正,调整了算法比例因子,从而推导了最佳状态链的确定算法、HMM参数优化算法。最后将研究开发的动态手势识别算法成功地应用到了基于网络的远程机器人控制系统中。

Vision based dynamic hand gesture recognition technology is studied, skin-color based Gauss model and a revised optical flow tracing algorithm are combined, and real-time hand gesture tracking in complex background is realized with characteristics of rapidity, accuracy and good robustness. Hidden Markov model （HMM） is adopted as training recognition algorithm for the dynamic hand gesture recognizer. Considering characteristics of dynamic hand gesture, the best state chain determination algorithm and HMM parameter optimization algorithm are deduced by modifying the reestimation formula of HMM parameter optimization algorithm and adjusting the scale factors of the algorithm. At last, the dynamic hand gesture recognition algorithm is successfully used in a network based remote robot control system.