Abstract
At present, part of people's body is in the state of sub-health, and more people pay attention to physical exercise. Dance is a relatively simple and popular activity, it has been widely concerned. 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 are 99.98, 97.95, and 0.97.96%, respectively. It can be seen that this new method can effectively improve the performance of dance action recognition.
Highlights
In recent years, with the rapid development of sensor technology in intelligent devices, many application fields such as human daily behavior recognition, health detection and health care guidance are rising rapidly (Li et al, 2018a; Yu et al, 2020)
This paper proposes a dance action recognition method based on a new Densenet network
An action image recognition method based on a new dense convolutional neural network is proposed for the dance action
Summary
With the rapid development of sensor technology in intelligent devices, many application fields such as human daily behavior recognition, health detection and health care guidance are rising rapidly (Li et al, 2018a; Yu et al, 2020). To solve these problems, this paper proposes a dance action recognition method based on a new Densenet network. Subtle features, reduce model parameters as much as possible, significantly improve the performance of the action recognition, and make the model more suitable for real-time application of action recognition, a small convolution kernel residual network is adopted in this paper. Based on ResNet-18, it is simplified into eight layers, which greatly reduces model parameters, saves storage space and running time It is more suitable for the dance action images. A densely Connected Network (DenseNet) is a neural Network model for optical image processing, which has a powerful feature extraction function (Gao et al, 2020).
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