Key point feature extraction algorithms for multimodal gesture in complex environments
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.
improving bacterial foraging optimization algorithmsconditional generation adversarial networkgabor filterdouble tree complex wavelet transformkey point feature extraction
万方数据
维普
