The traditional action recognition method is easily affected by the action speed, illumination, occlusion and complex background, which leads to the poor robustness of the recognition results. In order to solve the above problems, an improved residual dense neural network method is used to study the automatic recognition of dance action images. Firstly, based on the residual model, the features of dance action are extracted by using the convolution layer and pooling layer. Then, the exponential linear element (ELU) activation function, batch normalization (BN) and Dropout technology are used to improve and optimize the model to mitigate the gradient disappearance, prevent over-fitting, accelerate convergence and enhance the model generalization ability. Finally, the dense connection network (DenseNet) is introduced to make the extracted dance action features more rich and effective. Comparison experiments are carried out on two public databases and one self-built database. The results show that the recognition rate of the proposed method on three databases is 99.98%, 97.95%.97.96%, respectively. It can be seen that this new method can effectively improve the performance of dance action recognition.

中文翻译：

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一种新的基于异构视图感知的舞蹈动作识别残差密集网络

传统的动作识别方法容易受到动作速度、光照、遮挡和复杂背景的影响，导致识别结果的鲁棒性较差。为了解决上述问题，采用一种改进的残差密集神经网络方法来研究舞蹈动作图像的自动识别。首先，基于残差模型，利用卷积层和池化层提取舞蹈动作的特征。然后，利用指数线性元素（ELU）激活函数、批量归一化（BN）和Dropout技术对模型进行改进和优化，以减轻梯度消失、防止过拟合、加速收敛和增强模型泛化能力。最后，引入密集连接网络（DenseNet），使提取的舞蹈动作特征更加丰富有效。在两个公共数据库和一个自建数据库上进行对比实验。结果表明，所提方法在三个数据库上的识别率分别为99.98%、97.95%.97.96%。可以看出，这种新方法可以有效提高舞蹈动作识别的性能。