Research on DeepLabV3+ Algorithm Based on Gesture Recognition
In order to solve the problems that multi-temporal and feature diversity are not considered in gesture recognition research,this paper proposes a gesture recognition extraction method based on improved DeeplabV3+model.By changing the ASPP module structure in the model,using multiple different void rates and image Pyramid Pooling and other operations,CBAM Attention Mechanism modules are added to improve the extraction accuracy and efficiency of the model.The results show that the training speed of improved DeeplabV3+model is improved by 29.2%,the recognition accuracy is improved by 0.04%,the similarity is improved by 0.68%,and the recall rate is improved by 0.36%.