# Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement

Authors: Wenhua Fan; Hyun Joo Min
Journal: Complexity
 (2020)

---

## Abstract

The structure of the deep artificial neural network is similar to the structure of the biological neural network, which can be well applied to the 3D visual image recognition of aerobics movements. A lot of results have been achieved by applying deep neural networks to the 3D visual image recognition of aerobics movements, but there are still many problems to be overcome. After analyzing the expression characteristics of the convolutional neural network model for the three-dimensional visual image characteristics of aerobics, this paper builds a convolutional neural network model. The model is improved on the basis of the traditional model and unifies the process of aerobics 3D visual image segmentation, target feature extraction, and target recognition. The convolutional neural network and the deep neural network based on autoencoder are designed and applied to aerobics action 3D visual image test set for recognition and comparison. We improve the accuracy of network recognition by adjusting the configuration parameters in the network model. The experimental results show that compared with other simple models, the model based on the improved AdaBoost algorithm can improve the final result significantly when the accuracy of each model is average. Therefore, the method can improve the recognition accuracy when multiple neural network models with general accuracy are obtained, thereby avoiding the complicated parameter adjustment process to obtain a single optimal network model.


---

## Body

## 1. Introduction

Aerobics is a kind of gymnastics, it is a kind of gymnastics that can change people’s physical and psychological feelings. As an independent event, the sport joined the International Gymnastics Federation in September 1994. In recent years, with the call of the global fitness craze and happy sports, the recognition of aerobics has become more and more widespread. The development of fitness aerobics and competitive aerobics conforms to the trend of the times [1, 2]. The essence of aerobics is to let people enjoy the feeling of joy through the beauty of athletes’ wonderful performances. This is a manifestation of high-grade spiritual civilization and wealth. It is precisely because of the fact that it attracts more people to participate.The three-dimensional visual image of aerobics is the basis of human perception of the world and provides people with rich and diverse information [3]. People have a strong ability to recognize the three-dimensional visual images of aerobics movements and are not even restricted by the sensory channel [4, 5]. People can not only recognize words with their eyes, but also when they write on the back of a person. Through the study of eye activity during the 3D visual image recognition of aerobics, it is found that changing the distance of the 3D visual image of aerobics and the position on the sensory organs will cause the size and shape of the 3D visual image of aerobics to change on the retina [6]. In the current 3D visual image recognition system for aerobics movements, the recognition of complex 3D visual images of aerobics movements can be realized mainly through different levels of information processing [7, 8]. For the familiar three-dimensional visual image of aerobics movements, once you have mastered its main features, you can use this main feature as a recognition unit without paying attention to its details [9]. The 3D visual image recognition of aerobics movements is an important application of artificial intelligence. Its main function is to compile a computer program that imitates the human brain to recognize the three-dimensional visual image of aerobics movements, process the three-dimensional visual image of aerobics movements, and extract effective information from it to realize the recognition of people or things [10, 11]. The development process of 3D visual image recognition of aerobics movements has mainly experienced three stages: text recognition, 3D visual image information processing, and recognition of aerobics movements and object recognition [12]. Now some of the relatively mature aerobics 3D visual image recognition technologies have been applied to the commercial field [13]. Due to the diversity of problems in the process of 3D visual image recognition of aerobics movements, the methods of 3D visual image recognition of aerobics movements can only adopt different methods for specific problems [14]. Therefore, many recognition systems require a lot of research to improve the performance of breakthroughs on specific issues (such as improving the efficiency of recognition and reducing the time required for system training). The method of machine learning is a method that can get better recognition results on different recognition problems [15]. Relevant scholars have adopted sparse coding that is more suitable for the three-dimensional visual image representation of aerobics to identify and recognize the target object, instead of vector quantization coding that has a large loss of accuracy [16]. The 3D visual image recognition technology of aerobics has been continuously improved. But we have to say that there are still many unresolved problems. The quality of the 3D visual image of aerobics action determines the effect of 3D visual image recognition of aerobics action, and the effect of 3D visual image recognition of aerobics action of poor quality is not good [17, 18]. There are still many issues that need to be studied further [19, 20]. In general, the 3D visual image recognition of aerobics movements is an extremely challenging task. It is difficult to achieve the desired effect with only one method. How to improve the accuracy of the 3D visual image recognition of aerobics movements and reduce the algorithm complexity of the system and how to make it practical are all worth studying.This paper improves on the mainstream convolutional neural network model VGG and builds a new CNN network model, which is called N-VGG. Combined with this model, a simple aerobics action three-dimensional visual image target positioning and recognition model is constructed. The activation function used in the traditional convolutional neural network model is analyzed, and a new activation function is introduced into the N-VGG network model. In the N-VGG network model, the spatial pyramid pooling technology (SPP) is introduced to improve the recognition accuracy of the model. This paper conducts multiple comparative experiments on convolutional neural networks and deep neural networks and shows that a single neural network can be adjusted with different parameters to improve its accuracy, but its parameter adjustment process is complicated and random. By comparing the experimental results of convolutional neural network and deep neural network fusion in single and different methods, it can be concluded that the model fusion method based on the improved AdaBoost algorithm can improve the neural network’s ability to improve the 3D visual image of aerobics movements to a certain extent.The rest of this article is organized as follows. Section2 studies the 3D visual image recognition technology and related algorithms of aerobics. Section 3 designs a convolutional neural network recognition model. In Section 4, simulation experiments and result analysis are carried out. Section 5 summarizes the full text.


## 2. Aerobics 3D Visual Image Recognition Technology and Related Algorithms Research

### 2.1. 3D Visual Image Processing of Aerobics

Aerobics 3D visual image processing refers to the use of a computer to process the 3D visual image of aerobics to be recognized to meet the subsequent needs of the recognition process. It is mainly divided into two types: aerobics 3D visual image preprocessing and aerobics 3D visual image segmentation. The preprocessing of 3D visual image of aerobics mainly includes restoration of 3D visual image of aerobics and transformation of 3D visual image of aerobics. Its main purpose is to remove the interference and noise in the 3D visual image of aerobics and enhance the 3D vision of aerobics. The useful information in the image improves the detectability of the target object. At the same time, due to the real-time requirements of the three-dimensional visual image processing of aerobics, it is necessary to re-encode and compress the three-dimensional visual image of aerobics to reduce the complexity and complexity of subsequent algorithms. The segmentation of aerobics 3D visual image is the process of segmenting the 3D visual image of aerobics to be recognized into several subregions. The features of each region have obvious differences, and the internal features of each region have certain similarities.The method based on edge segmentation is to segment the 3D visual image of aerobics by detecting the area where the gray value of the pixel changes suddenly, or the place where the texture structure changes suddenly. The edge is usually located at the connection between two different areas. Because in a 3D visual image of aerobics action, the gray values of different areas are often different, and there will be obvious gray discontinuities at the junction of the two areas, that is, the edge. Because the gray value of each pixel at the edge is discontinuously distributed, differential or second-order differential can be used for detection. They calculate the first-order differential of the gray value of each pixel distributed in the edge area; then the pixel corresponding to the place where its extreme value appears is the edge point of the three-dimensional visual image of aerobics action. The gray value of a point is calculated for the second-order differential, and the pixel point with a differential value of zero is also the edge point of the three-dimensional visual image of aerobics. Therefore, the edge detection of the three-dimensional visual image of aerobics can be performed by the method of differential operators.Because the Roberts differential detection operator uses an even number of templates, the gradient amplitude value at (x, y) is actually the value at the intersection shown in Figure 1, which is offset by the pixel from the real position, so this method will produce a wider response near the edge of the 3D visual image of aerobics, and the edge positioning accuracy is not high.Figure 1
Schematic diagram of Roberts operator algorithm.


### 2.2. Feature Extraction of the 3D Visual Image of Aerobics

The feature extraction of the 3D visual image of aerobics is the key step in the three steps of 3D visual image recognition of aerobics. It refers to the 3D vision of aerobics that remains unchanged after the interference of factors such as scaling, translation, scale transformation, or illumination. Image information is extracted as features, and the three-dimensional visual image of aerobics is abstracted into some concrete mathematical representations or vector descriptions. The feature extraction of aerobics action three-dimensional visual image can be divided into global features and local features according to the different extraction ranges. The global features of the three-dimensional visual image of aerobics refers to all the features that describe the overall information of the three-dimensional visual image of aerobics, such as the color feature, shape feature, and texture feature of the three-dimensional visual image of aerobics. The local features refer to the partial features of the 3D visual image of aerobics movements in a specific area. It is only a representation of the local features in a certain area in the 3D visual image of aerobics movements. It is only suitable for matching the 3D visual image of aerobics movements.The color histogram is the most widely used color feature in the 3D visual image retrieval of aerobics. It mainly uses the statistical distribution and basic tones of different colors in the entire 3D visual image of aerobics. It does not care about each color in aerobics. Each aerobics action 3D visual image can find its unique color histogram, so it is especially suitable for describing the 3D visual image of aerobics action that cannot be automatically segmented. The color histogram can be divided into two description methods: feature statistical histogram and feature cumulative histogram.Suppose the total number of pixels in the three-dimensional visual imageP of aerobics action with feature value xi is s (xi), and N is the total number of pixels in the three-dimensional visual image of aerobics action. The proportion of the total pixels in the 3D visual image is(1)hxi=sxi∑jsxi.That is to say, the feature histogram with featurex in the three-dimensional visual image P of aerobics can be expressed as(2)HP=hx1,hx2,hx3,…,hxn−1,hxn.Among them,n represents the number of features x. The cumulative histogram with feature x in the three-dimensional visual image P of aerobics movements can be expressed as(3)uP=ux1,ux2,ux3,…,uxn−1,uxn.Therefore, the feature statistical histogram and feature accumulation histogram of the three-dimensional visual image of aerobics can be regarded as two one-dimensional discrete functions, that is, to obtain the probability distribution of a certain feature in the three-dimensional visual image of aerobics. The feature histogram is the probability distribution of the grayscale of the three-dimensional visual image of aerobics.


### 2.3. Discriminant Deep Recognition Structure

The main function of discriminative depth structure is to distinguish pattern recognition and describe the posterior probability distribution of data. Because CNN contains multiple levels of network structure, it can ensure the minimization of preprocessed data, rely on shared weights to reduce the complexity of the model structure, and use spatial relationships to reduce the number of parameters. It is a learning algorithm that can perform better training. Unlike the DBN network, it is a discriminative deep structure. The CNN algorithm model structure is mainly composed of convolutional layer, pooling layer, and fully connected layer, as shown in Figure2. Using the CNN model structure to process the three-dimensional visual image of aerobics can make full use of the two-dimensional structure of the three-dimensional visual image data of aerobics to perform good feature extraction on the three-dimensional visual image of aerobics. Therefore, compared with other deep learning models, CNN has a higher accuracy in the recognition of 3D visual images of aerobics.Figure 2
Model diagram of 3D visual image recognition of aerobics based on CNN.CNN first convolves the input aerobics action three-dimensional visual image with the convolution kernel of the convolution layer and the bias that can be added. The basic mathematical expression of convolution in calculus is(4)St=∫wa⋅xt−ada.The discrete form is expressed as(5)st=∑awa⋅xt−a.For the two-dimensional convolution operation in the CNN network, the mathematical expression is(6)si,j=∑m∑nwm,n⋅xi+m,j+n.Among them,W is the convolution kernel in the convolution layer, and X is the input data. If X is a two-dimensional input matrix, W is also a two-dimensional matrix. If X is a multidimensional vector, then W is also a multidimensional vector. When the convolution layer performs a convolution operation on the input aerobics action three-dimensional visual image, it is the convolution kernel matrix and the input aerobics action three-dimensional visual image. The elements in each position of the different local matrix are multiplied and then added, but the operation is a kind of linear operation, while the neural network requires that each neuron in the network can adapt to complex nonlinear operations, which requires the introduction of nonlinear factors, namely, activation functions.In the CNN model, there are multiple convolution kernels in the convolutional layer. After each convolution kernel performs the convolution operation, the feature map of the input aerobics action 3D visual image can be obtained. These feature maps share the same weight matrix and bias. After the convolution results are averaged, the 3D visual image of the characteristic aerobics action is output, namely,(7)Yj=∑i=1NXi⋅WjN,where Xi represents the feature map extracted by the ith input, Wj represents the weight matrix of the jth convolution kernel, N represents the total number of input feature maps, and Yj represents the output jth feature map.After the 3D visual image of aerobics moves through the convolutional layer to obtain the feature map of the 3D visual image of the aerobics movement, it is sent to the pooling layer to aggregate and count each feature. The pooling layer is mainly responsible for the aggregation of the three-dimensional visual images of the characteristic aerobics actions, using statistical features to obtain the average or maximum value of the three-dimensional visual images of the aerobics actions in the characteristic area, and removing irrelevant feature samples to further reduce the number of parameters. The use of pooling operations on the continuous areas in the 3D visual image of aerobics can reduce the effect of translation, rotation, and other factors on the 3D visual image of aerobics and prevent overfitting.The fully connected layer is the “recognizer” of the entire CNN network. Its main function is to map the feature representation after operations such as convolution and pooling to the corresponding sample label space. Its essence is to use several 1 × 1 volumes. The product core performs convolution compression on the upper layer feature map, transforms the two-dimensional feature expression into a one-dimensional vector representation, and maps the original feature to each implicit semantic node.


## 2.1. 3D Visual Image Processing of Aerobics

Aerobics 3D visual image processing refers to the use of a computer to process the 3D visual image of aerobics to be recognized to meet the subsequent needs of the recognition process. It is mainly divided into two types: aerobics 3D visual image preprocessing and aerobics 3D visual image segmentation. The preprocessing of 3D visual image of aerobics mainly includes restoration of 3D visual image of aerobics and transformation of 3D visual image of aerobics. Its main purpose is to remove the interference and noise in the 3D visual image of aerobics and enhance the 3D vision of aerobics. The useful information in the image improves the detectability of the target object. At the same time, due to the real-time requirements of the three-dimensional visual image processing of aerobics, it is necessary to re-encode and compress the three-dimensional visual image of aerobics to reduce the complexity and complexity of subsequent algorithms. The segmentation of aerobics 3D visual image is the process of segmenting the 3D visual image of aerobics to be recognized into several subregions. The features of each region have obvious differences, and the internal features of each region have certain similarities.The method based on edge segmentation is to segment the 3D visual image of aerobics by detecting the area where the gray value of the pixel changes suddenly, or the place where the texture structure changes suddenly. The edge is usually located at the connection between two different areas. Because in a 3D visual image of aerobics action, the gray values of different areas are often different, and there will be obvious gray discontinuities at the junction of the two areas, that is, the edge. Because the gray value of each pixel at the edge is discontinuously distributed, differential or second-order differential can be used for detection. They calculate the first-order differential of the gray value of each pixel distributed in the edge area; then the pixel corresponding to the place where its extreme value appears is the edge point of the three-dimensional visual image of aerobics action. The gray value of a point is calculated for the second-order differential, and the pixel point with a differential value of zero is also the edge point of the three-dimensional visual image of aerobics. Therefore, the edge detection of the three-dimensional visual image of aerobics can be performed by the method of differential operators.Because the Roberts differential detection operator uses an even number of templates, the gradient amplitude value at (x, y) is actually the value at the intersection shown in Figure 1, which is offset by the pixel from the real position, so this method will produce a wider response near the edge of the 3D visual image of aerobics, and the edge positioning accuracy is not high.Figure 1
Schematic diagram of Roberts operator algorithm.


## 2.2. Feature Extraction of the 3D Visual Image of Aerobics

The feature extraction of the 3D visual image of aerobics is the key step in the three steps of 3D visual image recognition of aerobics. It refers to the 3D vision of aerobics that remains unchanged after the interference of factors such as scaling, translation, scale transformation, or illumination. Image information is extracted as features, and the three-dimensional visual image of aerobics is abstracted into some concrete mathematical representations or vector descriptions. The feature extraction of aerobics action three-dimensional visual image can be divided into global features and local features according to the different extraction ranges. The global features of the three-dimensional visual image of aerobics refers to all the features that describe the overall information of the three-dimensional visual image of aerobics, such as the color feature, shape feature, and texture feature of the three-dimensional visual image of aerobics. The local features refer to the partial features of the 3D visual image of aerobics movements in a specific area. It is only a representation of the local features in a certain area in the 3D visual image of aerobics movements. It is only suitable for matching the 3D visual image of aerobics movements.The color histogram is the most widely used color feature in the 3D visual image retrieval of aerobics. It mainly uses the statistical distribution and basic tones of different colors in the entire 3D visual image of aerobics. It does not care about each color in aerobics. Each aerobics action 3D visual image can find its unique color histogram, so it is especially suitable for describing the 3D visual image of aerobics action that cannot be automatically segmented. The color histogram can be divided into two description methods: feature statistical histogram and feature cumulative histogram.Suppose the total number of pixels in the three-dimensional visual imageP of aerobics action with feature value xi is s (xi), and N is the total number of pixels in the three-dimensional visual image of aerobics action. The proportion of the total pixels in the 3D visual image is(1)hxi=sxi∑jsxi.That is to say, the feature histogram with featurex in the three-dimensional visual image P of aerobics can be expressed as(2)HP=hx1,hx2,hx3,…,hxn−1,hxn.Among them,n represents the number of features x. The cumulative histogram with feature x in the three-dimensional visual image P of aerobics movements can be expressed as(3)uP=ux1,ux2,ux3,…,uxn−1,uxn.Therefore, the feature statistical histogram and feature accumulation histogram of the three-dimensional visual image of aerobics can be regarded as two one-dimensional discrete functions, that is, to obtain the probability distribution of a certain feature in the three-dimensional visual image of aerobics. The feature histogram is the probability distribution of the grayscale of the three-dimensional visual image of aerobics.


## 2.3. Discriminant Deep Recognition Structure

The main function of discriminative depth structure is to distinguish pattern recognition and describe the posterior probability distribution of data. Because CNN contains multiple levels of network structure, it can ensure the minimization of preprocessed data, rely on shared weights to reduce the complexity of the model structure, and use spatial relationships to reduce the number of parameters. It is a learning algorithm that can perform better training. Unlike the DBN network, it is a discriminative deep structure. The CNN algorithm model structure is mainly composed of convolutional layer, pooling layer, and fully connected layer, as shown in Figure2. Using the CNN model structure to process the three-dimensional visual image of aerobics can make full use of the two-dimensional structure of the three-dimensional visual image data of aerobics to perform good feature extraction on the three-dimensional visual image of aerobics. Therefore, compared with other deep learning models, CNN has a higher accuracy in the recognition of 3D visual images of aerobics.Figure 2
Model diagram of 3D visual image recognition of aerobics based on CNN.CNN first convolves the input aerobics action three-dimensional visual image with the convolution kernel of the convolution layer and the bias that can be added. The basic mathematical expression of convolution in calculus is(4)St=∫wa⋅xt−ada.The discrete form is expressed as(5)st=∑awa⋅xt−a.For the two-dimensional convolution operation in the CNN network, the mathematical expression is(6)si,j=∑m∑nwm,n⋅xi+m,j+n.Among them,W is the convolution kernel in the convolution layer, and X is the input data. If X is a two-dimensional input matrix, W is also a two-dimensional matrix. If X is a multidimensional vector, then W is also a multidimensional vector. When the convolution layer performs a convolution operation on the input aerobics action three-dimensional visual image, it is the convolution kernel matrix and the input aerobics action three-dimensional visual image. The elements in each position of the different local matrix are multiplied and then added, but the operation is a kind of linear operation, while the neural network requires that each neuron in the network can adapt to complex nonlinear operations, which requires the introduction of nonlinear factors, namely, activation functions.In the CNN model, there are multiple convolution kernels in the convolutional layer. After each convolution kernel performs the convolution operation, the feature map of the input aerobics action 3D visual image can be obtained. These feature maps share the same weight matrix and bias. After the convolution results are averaged, the 3D visual image of the characteristic aerobics action is output, namely,(7)Yj=∑i=1NXi⋅WjN,where Xi represents the feature map extracted by the ith input, Wj represents the weight matrix of the jth convolution kernel, N represents the total number of input feature maps, and Yj represents the output jth feature map.After the 3D visual image of aerobics moves through the convolutional layer to obtain the feature map of the 3D visual image of the aerobics movement, it is sent to the pooling layer to aggregate and count each feature. The pooling layer is mainly responsible for the aggregation of the three-dimensional visual images of the characteristic aerobics actions, using statistical features to obtain the average or maximum value of the three-dimensional visual images of the aerobics actions in the characteristic area, and removing irrelevant feature samples to further reduce the number of parameters. The use of pooling operations on the continuous areas in the 3D visual image of aerobics can reduce the effect of translation, rotation, and other factors on the 3D visual image of aerobics and prevent overfitting.The fully connected layer is the “recognizer” of the entire CNN network. Its main function is to map the feature representation after operations such as convolution and pooling to the corresponding sample label space. Its essence is to use several 1 × 1 volumes. The product core performs convolution compression on the upper layer feature map, transforms the two-dimensional feature expression into a one-dimensional vector representation, and maps the original feature to each implicit semantic node.


## 3. Design of the Convolutional Neural Network Recognition Model

### 3.1. Design of Activation Function

One problem with the traditional activation function is the disappearance of the gradient. From the previous back propagation algorithm derivation process, it can be seen that when the error value is reverse gradient descent, it must be multiplied by the current inputx-value. Taking the sigmoid function as an example, Grad = Error ∗ Sigmoid’ (x) ∗ x. Both types of functions have the characteristic of double-ended “saturation”; that is, the value range is limited. If the derivative value is in the (0, 1) interval, the derivative value is scaled, and if the x-value is in (0, 1) or (−1, 1), the saturation value is scaled. In this way, the error value in the propagation process of each layer is reduced at a double speed. After multiple recursive back propagations, the gradient continues to attenuate and disappear, making the process of network model learning gradually slow. This is the vanishing gradient problem caused by sigmoid-like functions.At this stage, the most commonly used activation function in neural network models is ReLUs. This function was created in a restricted Bozeman machine and has been used extensively in neural network models. The ReLUs activation function is the parameter entity itself when the parameter is positive and has a value of 0 when the parameter is negative, and its positive gradient is 1, and only one end is saturated. Based on this feature, the ReLUs function has two main characteristics: one is that the neurons output from it are more sparse; the other is that it can reduce the problem of gradient disappearance to a certain extent.However, ReLUs is a nonnegative function, and its output activation average value is greater than zero. The mean value of the neural unit is nonzero, and as the input of the next layer, there will be an offset difference. In this way, with the gradual accumulation, the higher the layer of neurons, the greater the offset difference. The effect of too large offset difference is to reduce the learning speed of the neural network model. Some solutions are to adjust the weight update during the gradient descent process, such as the SReLUs function.In order to make the average output of neurons tend to 0 as much as possible, some variants of ReLUs activation function are produced. For example, the function Leaky ReLUs replaces the 0 value part of ReLUs with a linear functionf (x) = аx, and other similar methods such as parameterized ReLUs and random leaky ReLUs approximate linear changes. Although the activation functions LReLUs, PReLUs, and RReLUs can make the average output of neurons 0, they still do not guarantee that neurons are in a noise-robust activation state.


### 3.2. Design of Convolution and Pooling

In the existing convolutional neural network model, the convolution operation is an indispensable operation, and there is no fixed requirement for the design of the convolution kernel size. In general, the size of the convolution kernel is related to the “depth” design of the model. In the case of the same input size of the 3D visual image of aerobics, the larger the convolution kernel of each layer, the faster the size of the feature map will shrink. This paper proposes that the N-VGG network is based on the VGG network transformation, in which the fixed value of the convolution kernel is set to the size of 3 ∗ 3, and the moving step is 1. There are some feature maps at the edge of the 3 ∗ 3 visual image of aerobics movements that cannot just meet the size of the 3 ∗ 3 sliding window. At this time, a “patch” operation is required; that is, the last few pixels of the 3D visual image of aerobics movements are insufficient.Existing convolutional neural network models generally use maximum pooling or average pooling. The difference between maximum pooling and mean pooling is the value of the function evaluation. There are also some pooling methods that are somewhat different in operation, such as overlapping pooling, which only sets the moving step of the sliding window to be smaller than the side length of the window. Therefore, adjacent pooling windows will have some overlapping areas, and the corresponding overlapping pooling generated features maps are also larger. Overlapping pooling can improve the recognition accuracy of CNN to a certain extent for the three-dimensional visual images of aerobics with tight final features. Here we introduce a new pooling method: Spatial Pyramid Pooling (SPP). The SPP pooling method utilizes the multiscale information of the pooled area and can solve the problem of different input sizes of the three-dimensional visual image of aerobics.The SPP pooling method comes from a defect in the traditional CNN model; that is, the size of the input aerobics action 3D visual image in the traditional CNN model is fixed, and the 3D visual image of aerobics action of different sizes is usually scaled. The zooming operation will cause the distortion of the three-dimensional visual image of the aerobics action to a certain extent, which may lose some pixel spatial information, and the recognition accuracy of the model will be affected. SPP pooling can map different sizes of aerobics 3D visual images to the same feature dimension. Since SPP can process 3D visual images of aerobics movements of any size, it can also avoid the zooming operation of feature maps or original maps, which can reduce the information loss of 3D visual images of aerobics movements to a certain extent, thereby improving the 3D visual images of aerobics movements characteristic expression ability. The specific operation is relatively simple, as shown in Figure3.Figure 3
Schematic of spatial pyramid pooling.The feature map is pooled into three feature maps, corresponding to 1, 4, and 16 feature points. The middle connection layer contains 256 feature points. These 256 feature points and the previous 21 feature points are, respectively, fully connected, and after being fully connected, they are cascaded into a 5376-dimensional feature vector. We perform the next step on this feature vector. Spatial pyramid pooling is to map feature maps of different scales to feature vectors of the same dimension in this way.


### 3.3. Recognizer Design

Support vector machine is a very classic recognition algorithm in the field of machine learning. “Machine” in the field of machine learning usually refers to algorithms, and support vectors refer to variables that can affect decisions.Suppose there are several training samples (xi, yi), which can be separated by the hyperplane L3: wx + b = 0. At this time, the distance between L3 and the closest sample point of the two categories is maximized. In the above linearly separable case, the parallel hyperplane line defining the support vector point is L1: w∗x + b = 1 and L2: w∗x + b = −1, then the distance between the two is 2/w; at this time, the maximum identification interval is maximized; that is, w is minimized. At this time, all sample points are satisfied:(8)xi⋅w≥−b+1,yi=1,xi⋅w<−b−1,yi=−1.At this point, the problem is equivalently defined as(9)minw,b0.5w2,s.t.yixi⋅w+b>1,i=1,2,…,n−1,n.The above problem becomes a minimum value solving problem under several inequality constraints, that is, an optimization problem of convex quadratic programming. To construct the Lagrange function for the above problem, we get(10)Lw,a,b=0.5wT⋅w−∑i=1nai⋅yixi⋅w+b−1.Among them,ai is the Lagrange multiplier. According to the Lagrange duality theory, the dual problem of the original problem can be obtained. This problem can be solved by the SMO algorithm, and the solution of w can be obtained as(11)w=∑i=0n−1ai⋅xi⋅yi.The decision function at this time is(12)fx=sgn∑i=0n−1ai⋅yi⋅x,xi+b.In this paper, the improved N-VGG convolutional neural network model based on the VGG network model is basically the same as the original VGG network model. The difference in the framework includes the introduction of the IRPN network at the last shared convolutional layer, which is used for model segmentation. The pooling layer after the convolutional layer is changed from the original maximum pooling layer to the spatial pyramid pooling layer.In addition, inspired by the IRPN network, a bounding box regression layer is also introduced in the recognition layer. The function of the bounding box regression layer is the same as the bounding box regression method in IRPN. Its main function is to correct the bounding box of the target during the test phase. The bounding box regression at this time is the regression correction of the original target and the predicted target, so the loss function is(13)Lp,u,v,tu=Lloctu,v+Lclsp,u.The pooling of the SPP layer is performed on the cells after cutting. Considering that the pixel space of 2 ∗ 2 cells is small enough, fully connected mapping is used for 2 ∗ 2 cells. Here, only 7 ∗ 7 and 4 ∗ 4 cells are pooled in spatial pyramid. Among them, the size of the intermediate connection layer is changed to 179 dimensions. At this time, the dimension of the first fully connected layer is 4053.


## 3.1. Design of Activation Function

One problem with the traditional activation function is the disappearance of the gradient. From the previous back propagation algorithm derivation process, it can be seen that when the error value is reverse gradient descent, it must be multiplied by the current inputx-value. Taking the sigmoid function as an example, Grad = Error ∗ Sigmoid’ (x) ∗ x. Both types of functions have the characteristic of double-ended “saturation”; that is, the value range is limited. If the derivative value is in the (0, 1) interval, the derivative value is scaled, and if the x-value is in (0, 1) or (−1, 1), the saturation value is scaled. In this way, the error value in the propagation process of each layer is reduced at a double speed. After multiple recursive back propagations, the gradient continues to attenuate and disappear, making the process of network model learning gradually slow. This is the vanishing gradient problem caused by sigmoid-like functions.At this stage, the most commonly used activation function in neural network models is ReLUs. This function was created in a restricted Bozeman machine and has been used extensively in neural network models. The ReLUs activation function is the parameter entity itself when the parameter is positive and has a value of 0 when the parameter is negative, and its positive gradient is 1, and only one end is saturated. Based on this feature, the ReLUs function has two main characteristics: one is that the neurons output from it are more sparse; the other is that it can reduce the problem of gradient disappearance to a certain extent.However, ReLUs is a nonnegative function, and its output activation average value is greater than zero. The mean value of the neural unit is nonzero, and as the input of the next layer, there will be an offset difference. In this way, with the gradual accumulation, the higher the layer of neurons, the greater the offset difference. The effect of too large offset difference is to reduce the learning speed of the neural network model. Some solutions are to adjust the weight update during the gradient descent process, such as the SReLUs function.In order to make the average output of neurons tend to 0 as much as possible, some variants of ReLUs activation function are produced. For example, the function Leaky ReLUs replaces the 0 value part of ReLUs with a linear functionf (x) = аx, and other similar methods such as parameterized ReLUs and random leaky ReLUs approximate linear changes. Although the activation functions LReLUs, PReLUs, and RReLUs can make the average output of neurons 0, they still do not guarantee that neurons are in a noise-robust activation state.


## 3.2. Design of Convolution and Pooling

In the existing convolutional neural network model, the convolution operation is an indispensable operation, and there is no fixed requirement for the design of the convolution kernel size. In general, the size of the convolution kernel is related to the “depth” design of the model. In the case of the same input size of the 3D visual image of aerobics, the larger the convolution kernel of each layer, the faster the size of the feature map will shrink. This paper proposes that the N-VGG network is based on the VGG network transformation, in which the fixed value of the convolution kernel is set to the size of 3 ∗ 3, and the moving step is 1. There are some feature maps at the edge of the 3 ∗ 3 visual image of aerobics movements that cannot just meet the size of the 3 ∗ 3 sliding window. At this time, a “patch” operation is required; that is, the last few pixels of the 3D visual image of aerobics movements are insufficient.Existing convolutional neural network models generally use maximum pooling or average pooling. The difference between maximum pooling and mean pooling is the value of the function evaluation. There are also some pooling methods that are somewhat different in operation, such as overlapping pooling, which only sets the moving step of the sliding window to be smaller than the side length of the window. Therefore, adjacent pooling windows will have some overlapping areas, and the corresponding overlapping pooling generated features maps are also larger. Overlapping pooling can improve the recognition accuracy of CNN to a certain extent for the three-dimensional visual images of aerobics with tight final features. Here we introduce a new pooling method: Spatial Pyramid Pooling (SPP). The SPP pooling method utilizes the multiscale information of the pooled area and can solve the problem of different input sizes of the three-dimensional visual image of aerobics.The SPP pooling method comes from a defect in the traditional CNN model; that is, the size of the input aerobics action 3D visual image in the traditional CNN model is fixed, and the 3D visual image of aerobics action of different sizes is usually scaled. The zooming operation will cause the distortion of the three-dimensional visual image of the aerobics action to a certain extent, which may lose some pixel spatial information, and the recognition accuracy of the model will be affected. SPP pooling can map different sizes of aerobics 3D visual images to the same feature dimension. Since SPP can process 3D visual images of aerobics movements of any size, it can also avoid the zooming operation of feature maps or original maps, which can reduce the information loss of 3D visual images of aerobics movements to a certain extent, thereby improving the 3D visual images of aerobics movements characteristic expression ability. The specific operation is relatively simple, as shown in Figure3.Figure 3
Schematic of spatial pyramid pooling.The feature map is pooled into three feature maps, corresponding to 1, 4, and 16 feature points. The middle connection layer contains 256 feature points. These 256 feature points and the previous 21 feature points are, respectively, fully connected, and after being fully connected, they are cascaded into a 5376-dimensional feature vector. We perform the next step on this feature vector. Spatial pyramid pooling is to map feature maps of different scales to feature vectors of the same dimension in this way.


## 3.3. Recognizer Design

Support vector machine is a very classic recognition algorithm in the field of machine learning. “Machine” in the field of machine learning usually refers to algorithms, and support vectors refer to variables that can affect decisions.Suppose there are several training samples (xi, yi), which can be separated by the hyperplane L3: wx + b = 0. At this time, the distance between L3 and the closest sample point of the two categories is maximized. In the above linearly separable case, the parallel hyperplane line defining the support vector point is L1: w∗x + b = 1 and L2: w∗x + b = −1, then the distance between the two is 2/w; at this time, the maximum identification interval is maximized; that is, w is minimized. At this time, all sample points are satisfied:(8)xi⋅w≥−b+1,yi=1,xi⋅w<−b−1,yi=−1.At this point, the problem is equivalently defined as(9)minw,b0.5w2,s.t.yixi⋅w+b>1,i=1,2,…,n−1,n.The above problem becomes a minimum value solving problem under several inequality constraints, that is, an optimization problem of convex quadratic programming. To construct the Lagrange function for the above problem, we get(10)Lw,a,b=0.5wT⋅w−∑i=1nai⋅yixi⋅w+b−1.Among them,ai is the Lagrange multiplier. According to the Lagrange duality theory, the dual problem of the original problem can be obtained. This problem can be solved by the SMO algorithm, and the solution of w can be obtained as(11)w=∑i=0n−1ai⋅xi⋅yi.The decision function at this time is(12)fx=sgn∑i=0n−1ai⋅yi⋅x,xi+b.In this paper, the improved N-VGG convolutional neural network model based on the VGG network model is basically the same as the original VGG network model. The difference in the framework includes the introduction of the IRPN network at the last shared convolutional layer, which is used for model segmentation. The pooling layer after the convolutional layer is changed from the original maximum pooling layer to the spatial pyramid pooling layer.In addition, inspired by the IRPN network, a bounding box regression layer is also introduced in the recognition layer. The function of the bounding box regression layer is the same as the bounding box regression method in IRPN. Its main function is to correct the bounding box of the target during the test phase. The bounding box regression at this time is the regression correction of the original target and the predicted target, so the loss function is(13)Lp,u,v,tu=Lloctu,v+Lclsp,u.The pooling of the SPP layer is performed on the cells after cutting. Considering that the pixel space of 2 ∗ 2 cells is small enough, fully connected mapping is used for 2 ∗ 2 cells. Here, only 7 ∗ 7 and 4 ∗ 4 cells are pooled in spatial pyramid. Among them, the size of the intermediate connection layer is changed to 179 dimensions. At this time, the dimension of the first fully connected layer is 4053.


## 4. Simulation Experiment and Result Analysis

### 4.1. Convolutional Neural Network Aerobics 3D Visual Image Recognition Simulation

By adjusting the configuration parameters of the convolutional neural network to improve the recognition accuracy of the network, we obtain relevant experimental data and analyze and discuss the ways and costs of improving the recognition results of the convolutional neural network.Since the 3 data sets of the experiment are all grayscale images with small pixels, according to the principle of convolutional neural network, suppose the image size isn∗n, the size of the first convolution kernel is c1∗c1, and the size of the downsampling area is s1∗s1; the output feature map size after processing by the first convolutional layer and downsampling layer is o1 = (n + 1 − c1)/s1. The division operation also needs to be divided. Therefore, in order to ensure that the convolution kernel and sampling area have the appropriate size, the designed convolutional neural network has only 2 convolutional layers and 2 downsampling layers. A small number of network layers can also prevent the backpropagation algorithm from transmitting the error update value from back to front without becoming too small due to too many layers. The final fully connected layer will be adjusted for the number of categories of different data sets.In the training method of the convolutional neural network, this experiment uses the BP algorithm for training; that is, first, we randomly initialize the convolution kernel matrix and then update the convolution kernel according to the error between the training result and the real result. Different from the BP algorithm, this experiment uses a method called min-batch for iteration. This method does not calculate the error of all training samples and update the weight parameters in each iteration; instead, the training samples are divided into multiple blocks. (Batch); each iteration only trains a sample of one block and adjusts the weight according to the error of the sample of the block. Since the weight update is performed for some samples, the learning rate can be increased appropriately to increase the update range of the weight on the partial samples, so that it can quickly adapt to all samples. Training the training samples in blocks helps to speed up the update speed of weights and improve training efficiency. The network structure (6, 5) − 2 − (12, 5) − 2 shows that the convolutional neural network has a total of 2 convolutional layers and 2 downsampling layers. There are 6 convolution kernels in the first convolutional layer. The size of each convolution kernel is 5 ∗ 5, and the size of the first downsampling area is 2 ∗ 2; there are 12 convolution kernels in the second convolution layer, and the size of each convolution kernel is 5 ∗ 5. The size of the two downsampling areas is 2 ∗ 2.Some parameters of the convolutional neural network are adjusted to obtain network configurations with different structures. Experiments were performed on these network models with different configurations to obtain experimental results. We conducted a total of 10 sets of experiments. Figure4 shows the test results of the convolutional neural network on the aerobics dataset.Figure 4
Test results of convolutional neural network on the data set. (a) Running time. (b) Error rate.
(a)(b)The convolutional neural network is trained in the min-batch method, and each block has a size of 50, which means 50 training samples are included. Before the experiment, the training samples were randomly shuffled to ensure that the sample distribution in each block is random. The training data set has a total of 80,000 samples, and parameter updates are performed after each block is trained. Therefore, a total of 80,000/50 = 1600 parameter updates are required for one iteration. Figure5 is the training error graph of experiment 1.Figure 5
3D visual image experiment of aerobics movement 1 training error.It can be seen from the error curve in Figure5 that the min-batch method of training makes the error of the network model fluctuate, but all are within a certain range. Because the update parameters are performed after each small sample of training, the sample is not global, so it is not ruled out that there may be jitter when the error drops, but the error is still acceptable from the overall situation.The network parameters used in experiment 1 and experiment 2 are exactly the same; the only difference is the sampling method. Experiment 1 uses mean sampling; experiment 2 uses maximum sampling. Experiment 2 is more advantageous in terms of efficiency. Because only the maximum value is taken, experiment 2 avoids the mean value calculation process. If the sampling area is large, the running time advantage of experiment 2 will be more obvious. And the maximum value sampling can well represent the difference of the data in the sampling area, reflect the most characteristic data in the area, reduce information loss, and improve the recognition accuracy. Therefore, in experiments 2 to 10, maximum sampling is used.Experiments 2, 5, and 6 used 3, 6, and 12 convolution kernels in the first convolution layer when the size of the convolution kernels was the same. As the running time becomes longer, the experimental results are not gradually improved. The specific relationship is shown in Figure6.Figure 6
The relationship between the number of convolution kernels and the recognition results.It can be seen from Figure6 that when the size of the convolution kernel is constant, the recognition accuracy shows a wave trend as the number of convolution kernels increases. This shows that the increase in the number of convolution kernels is beneficial to the extraction of more features, thereby improving the recognition effect. However, when a certain number of convolution kernels is reached, the recognition effect is not the highest. The reason is that the extracted features are so many that redundant noise is also extracted, resulting in an overfitting phenomenon, resulting in a reduction in the recognition result.When the number of the first layer of convolution kernels is certain, increasing the size of the convolution kernel can improve the recognition result because the increase of the convolution kernel can enlarge the area of the extracted features, so that the associated features in the area can be better extracted. However, a too large convolution kernel will also cause a decrease in results because the expansion of the extraction area leads to a loss of information. The comparative experiments 2, 3, and 4 are shown in Figure7.Figure 7
The relationship between the size of the convolution kernel and the recognition result.Comparing experiments 3, 7, and 8, the network structures used by them are exactly the same, but experiment 2 is iterated once during training; experiment 7 is iterated 5 times, a total of 1600 ∗ 5 = 8000 parameter updates; and experiment 8 iterates 50 times, a total of 1600 ∗ 50 = 80000 parameter updates. Figure8 shows the relationship between training time and recognition accuracy. According to the curve, we can find that increasing the training time in the first part can effectively improve the accuracy of the model, but the accuracy of the latter part will increase slowly or even decrease. Therefore, increasing the training time does not produce a significant improvement in accuracy. Due to the limitations of hardware conditions, this experiment did not conduct a higher number of iterations, but it is theoretically expected that as the number of iterations increases, the network may overfit the training data set, thereby affecting the results on the test data set.Figure 8
The relationship between training time and recognition accuracy.


### 4.2. Deep Neural Network Aerobics 3D Visual Image Recognition Simulation

The deep learning neural network designed in this section is based on an autoencoder, stacking multiple autoencoders, and the hidden layer of the previous encoder is used as the input of the latter encoder, thus forming a multi-autoencoder deep neural network. Because the hidden layer of the autoencoder extracts the potential features of the data, using it as the input of the subsequent autoencoder can further extract the features to obtain more abstract high-level features. The results of the 3D visual image recognition of aerobics by the deep neural network are shown in Figure9.Figure 9
Recognition result of 3D visual image of aerobics action by deep neural network.By adjusting the structure and configuration of the deep neural network, we got the following experimental results. Figure10 shows the test results of the network model on the data set.Figure 10
Test results of the deep neural network on the data set. (a) Running time. (b) Error rate.
(a)(b)Increasing the number of hidden layers can also increase the final accuracy to a certain extent, but the accuracy will decrease when it reaches a certain number. The reason for the analysis is that increasing the number of hidden layers means increasing the number of abstractions of shallow features, which makes the later features more advanced, thus improving the recognition results. However, the autoencoder used in the experiment will minimize the error when extracting features of each layer, but this does not mean that no error will be generated. The increase in the number of layers will lead to the accumulation of errors, so that the error will be reduced to a certain extent. According to Figure11, we can observe that when the number of features is relatively large, the increase in the number of hidden layers can significantly improve the recognition accuracy.Figure 11
Comparison of the number of hidden layers and the number of nodes of the autoencoder.


## 4.1. Convolutional Neural Network Aerobics 3D Visual Image Recognition Simulation

By adjusting the configuration parameters of the convolutional neural network to improve the recognition accuracy of the network, we obtain relevant experimental data and analyze and discuss the ways and costs of improving the recognition results of the convolutional neural network.Since the 3 data sets of the experiment are all grayscale images with small pixels, according to the principle of convolutional neural network, suppose the image size isn∗n, the size of the first convolution kernel is c1∗c1, and the size of the downsampling area is s1∗s1; the output feature map size after processing by the first convolutional layer and downsampling layer is o1 = (n + 1 − c1)/s1. The division operation also needs to be divided. Therefore, in order to ensure that the convolution kernel and sampling area have the appropriate size, the designed convolutional neural network has only 2 convolutional layers and 2 downsampling layers. A small number of network layers can also prevent the backpropagation algorithm from transmitting the error update value from back to front without becoming too small due to too many layers. The final fully connected layer will be adjusted for the number of categories of different data sets.In the training method of the convolutional neural network, this experiment uses the BP algorithm for training; that is, first, we randomly initialize the convolution kernel matrix and then update the convolution kernel according to the error between the training result and the real result. Different from the BP algorithm, this experiment uses a method called min-batch for iteration. This method does not calculate the error of all training samples and update the weight parameters in each iteration; instead, the training samples are divided into multiple blocks. (Batch); each iteration only trains a sample of one block and adjusts the weight according to the error of the sample of the block. Since the weight update is performed for some samples, the learning rate can be increased appropriately to increase the update range of the weight on the partial samples, so that it can quickly adapt to all samples. Training the training samples in blocks helps to speed up the update speed of weights and improve training efficiency. The network structure (6, 5) − 2 − (12, 5) − 2 shows that the convolutional neural network has a total of 2 convolutional layers and 2 downsampling layers. There are 6 convolution kernels in the first convolutional layer. The size of each convolution kernel is 5 ∗ 5, and the size of the first downsampling area is 2 ∗ 2; there are 12 convolution kernels in the second convolution layer, and the size of each convolution kernel is 5 ∗ 5. The size of the two downsampling areas is 2 ∗ 2.Some parameters of the convolutional neural network are adjusted to obtain network configurations with different structures. Experiments were performed on these network models with different configurations to obtain experimental results. We conducted a total of 10 sets of experiments. Figure4 shows the test results of the convolutional neural network on the aerobics dataset.Figure 4
Test results of convolutional neural network on the data set. (a) Running time. (b) Error rate.
(a)(b)The convolutional neural network is trained in the min-batch method, and each block has a size of 50, which means 50 training samples are included. Before the experiment, the training samples were randomly shuffled to ensure that the sample distribution in each block is random. The training data set has a total of 80,000 samples, and parameter updates are performed after each block is trained. Therefore, a total of 80,000/50 = 1600 parameter updates are required for one iteration. Figure5 is the training error graph of experiment 1.Figure 5
3D visual image experiment of aerobics movement 1 training error.It can be seen from the error curve in Figure5 that the min-batch method of training makes the error of the network model fluctuate, but all are within a certain range. Because the update parameters are performed after each small sample of training, the sample is not global, so it is not ruled out that there may be jitter when the error drops, but the error is still acceptable from the overall situation.The network parameters used in experiment 1 and experiment 2 are exactly the same; the only difference is the sampling method. Experiment 1 uses mean sampling; experiment 2 uses maximum sampling. Experiment 2 is more advantageous in terms of efficiency. Because only the maximum value is taken, experiment 2 avoids the mean value calculation process. If the sampling area is large, the running time advantage of experiment 2 will be more obvious. And the maximum value sampling can well represent the difference of the data in the sampling area, reflect the most characteristic data in the area, reduce information loss, and improve the recognition accuracy. Therefore, in experiments 2 to 10, maximum sampling is used.Experiments 2, 5, and 6 used 3, 6, and 12 convolution kernels in the first convolution layer when the size of the convolution kernels was the same. As the running time becomes longer, the experimental results are not gradually improved. The specific relationship is shown in Figure6.Figure 6
The relationship between the number of convolution kernels and the recognition results.It can be seen from Figure6 that when the size of the convolution kernel is constant, the recognition accuracy shows a wave trend as the number of convolution kernels increases. This shows that the increase in the number of convolution kernels is beneficial to the extraction of more features, thereby improving the recognition effect. However, when a certain number of convolution kernels is reached, the recognition effect is not the highest. The reason is that the extracted features are so many that redundant noise is also extracted, resulting in an overfitting phenomenon, resulting in a reduction in the recognition result.When the number of the first layer of convolution kernels is certain, increasing the size of the convolution kernel can improve the recognition result because the increase of the convolution kernel can enlarge the area of the extracted features, so that the associated features in the area can be better extracted. However, a too large convolution kernel will also cause a decrease in results because the expansion of the extraction area leads to a loss of information. The comparative experiments 2, 3, and 4 are shown in Figure7.Figure 7
The relationship between the size of the convolution kernel and the recognition result.Comparing experiments 3, 7, and 8, the network structures used by them are exactly the same, but experiment 2 is iterated once during training; experiment 7 is iterated 5 times, a total of 1600 ∗ 5 = 8000 parameter updates; and experiment 8 iterates 50 times, a total of 1600 ∗ 50 = 80000 parameter updates. Figure8 shows the relationship between training time and recognition accuracy. According to the curve, we can find that increasing the training time in the first part can effectively improve the accuracy of the model, but the accuracy of the latter part will increase slowly or even decrease. Therefore, increasing the training time does not produce a significant improvement in accuracy. Due to the limitations of hardware conditions, this experiment did not conduct a higher number of iterations, but it is theoretically expected that as the number of iterations increases, the network may overfit the training data set, thereby affecting the results on the test data set.Figure 8
The relationship between training time and recognition accuracy.


## 4.2. Deep Neural Network Aerobics 3D Visual Image Recognition Simulation

The deep learning neural network designed in this section is based on an autoencoder, stacking multiple autoencoders, and the hidden layer of the previous encoder is used as the input of the latter encoder, thus forming a multi-autoencoder deep neural network. Because the hidden layer of the autoencoder extracts the potential features of the data, using it as the input of the subsequent autoencoder can further extract the features to obtain more abstract high-level features. The results of the 3D visual image recognition of aerobics by the deep neural network are shown in Figure9.Figure 9
Recognition result of 3D visual image of aerobics action by deep neural network.By adjusting the structure and configuration of the deep neural network, we got the following experimental results. Figure10 shows the test results of the network model on the data set.Figure 10
Test results of the deep neural network on the data set. (a) Running time. (b) Error rate.
(a)(b)Increasing the number of hidden layers can also increase the final accuracy to a certain extent, but the accuracy will decrease when it reaches a certain number. The reason for the analysis is that increasing the number of hidden layers means increasing the number of abstractions of shallow features, which makes the later features more advanced, thus improving the recognition results. However, the autoencoder used in the experiment will minimize the error when extracting features of each layer, but this does not mean that no error will be generated. The increase in the number of layers will lead to the accumulation of errors, so that the error will be reduced to a certain extent. According to Figure11, we can observe that when the number of features is relatively large, the increase in the number of hidden layers can significantly improve the recognition accuracy.Figure 11
Comparison of the number of hidden layers and the number of nodes of the autoencoder.


## 5. Conclusion

This paper, through the research of aerobics action 3D visual image recognition and deep learning technology, introduces in detail the aerobics action 3D visual image recognition and deep learning theory, summarizes the description of common algorithms for feature extraction, and elaborates common aerobics such as R-CNN. The action 3D visual image recognition algorithm model and performance indicators verify the performance of the algorithm model. This paper introduces a new activation function ELU to replace the traditional functions such as ReLU. In addition, the maximum pooling layer of the last layer of the N-VGG model becomes spatial pyramid pooling. We train an SVM recognizer in the test phase to compare the recognition performance of the softmax recognizer. Convolutional neural network and autoencoder-based deep learning neural network are used as experimental objects, and they are applied to the benchmark data set for recognition testing. By adjusting the network configuration parameters and conducting multiple comparison experiments, the accuracy of the neural network model is improved. In the experiment, the model fusion method based on the improved AdaBoost algorithm is implemented and the above neural network model is fused. By comparing the experimental results of the model, we conclude that the model fusion method based on the improved AdaBoost algorithm is not accurate in the fusion model. In the case, when there is only a high accuracy model, there is also an improvement, but the effect is not as obvious as the first case.



---

*Source: 8889008-2020-11-12.xml*