Aiming at the problem of the absence of detail texture and other high-frequency features in the feature extraction process of the deep network employing the upsampling operation, the accuracy of gesture recognition is seriously affected in complex scenes. This study integrates object detection and gesture recognition into one model and proposes a gesture detection and recognition based on the pyramid frequency feature fusion module and multiscale attention in human-computer interaction. Pyramid fusion module is used to perform efficient feature fusion and is proposed to obtain feature layers with rich details and semantic information, which is helpful to improve the efficiency and accuracy of gesture recognition. In addition, the multiscale attention module is further adopted to adaptively mine important and effective feature information from both temporal and spatial channels and embedded into the detection layer. Finally, our proposed network realizes the enhancement of the effective information and the suppression of the invalid information of the detection layer. Experimental results show that our proposed model makes full use of the high-low frequency feature fusion module without replacing the basic backbone network, which can greatly reduce the computational overhead while improving the detection accuracy.

中文翻译：

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基于金字塔频率特征融合模块和多尺度注意力的人机交互手势检测与识别

针对采用上采样操作的深度网络的特征提取过程中缺少细节纹理和其他高频特征的问题，在复杂场景中手势识别的准确性受到严重影响。这项研究将对象检测和手势识别集成到一个模型中，并提出了一种基于金字塔频率特征融合模块和人机交互中多尺度注意力的手势检测和识别方法。金字塔融合模块用于进行有效的特征融合，并提出获取具有丰富细节和语义信息的特征层，有助于提高手势识别的效率和准确性。此外，进一步采用多尺度关注模块，从时空通道自适应挖掘重要有效信息，并嵌入检测层。最后，我们提出的网络实现了有效信息的增强和检测层无效信息的抑制。实验结果表明，该模型在不取代基本骨干网的情况下，充分利用了高低频特征融合模块，可以大大减少计算量，同时提高了检测精度。