基于多尺度卷积神经网络的连续手语精准识别研究OA

To capture features of different scales simultaneously and distinguish foreground gestures from background interference accurately,a continuous sign language accurate recognition method based on multi-scale convolutional neural networks is studied,aiming to solve the recognition difficulties caused by gesture diversity.A sign language sentence segmentation algorithm utilizing dominant hand trajectory information is used to detect transitional actions in continuous sign language videos,segment continuous sign language videos,and obtain multiple composite video segments.Multi-scale convolutional neural networks are used to capture features of different scales in each composite video segment by convolution kernels of different sizes,so as to distinguish foreground gestures from background interference accurately.A multi-scale dilated convolution pooling pyramid module is used to fuse the multi-scale features of each composite video segment,and the multi-scale information of sign language actions are fully utilized to enhance the network's ability to handle gesture diversity.A Softmax classifier is used to process and fuse multi-scale features,and accurate sign language recognition results for each composite video segment are obtained.The recognition results are concatenated in chronological order to obtain the final recognition results.Experimental results have shown that the method can recognize continuous sign language accurately,and its determination coefficient of continuous sign language recognition under different background interference conditions is close to 1,indicating high accuracy in continuous sign language recognition.To sum up,the proposed method can effectively solve the difficulties in continuous sign language recognition.