基于残差双注意力与跨级特征融合模块的静态手势识别

为解决卷积神经网络提取特征遗漏、手势多特征提取不充分问题, 本文提出基于残差双注意力与跨级特征融合模块的静态手势识别方法. 设计了一种残差双注意力模块, 该模块对ResNet50网络提取的低层特征进行增强, 能够有效学习关键信息并更新权重, 提高对高层特征的注意力, 然后由跨级特征融合模块对不同阶段的高低层特征进行融合, 丰富高级特征图中不同层级之间的语义和位置信息, 最后使用全连接层的Softmax分类器对手势图像进行分类识别. 本文在ASL美国手语数据集上进行实验, 平均准确率为99.68%, 相比基础ResNet50网络准确率提升2.52%. 结果验证本文方法能充分提取与复用手势特征, 有效提高手势图像的识别精度.

Static Gesture Recognition Based on Residual Double Attention Module and Cross-level Feature Fusion

To solve the problems of missing feature extraction by convolutional neural network and insufficient multi-feature extraction of a gesture, this study proposes a static gesture recognition method based on a residual double attention module and a cross-level feature fusion module. The designed residual double attention module can enhance the low-level features extracted by a ResNet50 network, effectively learn the key information, update the weight, and improve the attention to high-level features. Then, the cross-level feature fusion module fuses the high-level and low-level features in different stages to enrich the semantic and location information between different levels in the high-level feature map. Finally, the Softmax classifier of the fully connected layer is used to classify and recognize the gesture image. The experiment is carried out on the American sign language (ASL) dataset. The average recognition accuracy is 99.68%, which is 2.52% higher than that of the basic ResNet50 network. The results show that the proposed method can fully extract and reuse gesture features and effectively improve the recognition accuracy of gesture images.