复杂环境下多模态手势关键点特征提取算法

Key point feature extraction algorithms for multimodal gesture in complex environments

现阶段的手势关键点特征提取在复杂背景环境下存在特征提取精度低等问题,为了解决传统方法中存在的问题,提出复杂环境下多模态手势关键点特征提取算法。首先,通过改进细菌觅食(BFO)优化算法对手势图像进行增强处理;其次,通过条件生成对抗网络对手势图像进行背景去除处理;最后,通过GIFT方法检测手势图像的关键点,并通过多尺度双树复小波变换方法和Gabor滤波方法对手势图像进行多模态手势关键点特征提取。实验结果表明:所提算法的手势关键点特征提取精度更高、效果更好。

At present,there are problems with low accuracy in feature extraction of gesture key points in complex background environments.In order to solve the problems existing in traditional methods,a multimodal gesture key point feature extraction algorithm research is proposed in complex environments.Firstly,the gesture image is enhanced by improving the bacterial foraging(BFO)optimization algorithm;Secondly,background removal is performed on gesture images through conditional generation of adversarial networks;Finally,the GIFT method is used to detect the key points of the gesture image,and the multimodal gesture key poiti-scale dual tree complex wavelet transform method and Gabor filtering method.The experimental results show that the proposed algorithm has higher accuracy and better performance in extracting gesture key point features.