Intelligent central control method of intelligent venue multimedia system

Abstract

The invention discloses an intelligent central control method of an intelligent venue multimedia system, and relates to the technical field of intelligent control. The method comprises the following steps: scanning a human body by a depth camera, and registering human body surface point cloud data by a human body posture recognition network; the human body posture recognition network recognizes the current posture of the current human body and matches the human body posture instruction library to obtain a current user instruction; and the control host issues the successfully matched user instruction to the corresponding multimedia system for operation and control. Three-dimensional point cloud collection is carried out on the human body in the designated area, registration is carried out on the human body surface point cloud data, and the human body posture recognition network recognizes the current posture or gesture of the current human body to match the user instruction to control the corresponding multimedia system, so that the intelligence and the regulation and control efficiency of the smart venue are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent control, and particularly relates to an intelligent central control method for a multimedia system in a smart venue. Background Art

[0002] In recent years, the display space of modern digital multimedia exhibition halls has been increasing. In exhibition halls, there are more and more applications of multimedia interactive display devices such as projectors, LED screens, splicing screens, slide screens, all-in-one machines, carousel lights, and speakers. The operation demonstrations of each exhibition item are also becoming more and more complex. Traditional manual operation has greatly reduced work efficiency and can no longer meet the management requirements and positioning of modern exhibition halls. The intelligent central control system has become an essential application in exhibition halls.

[0003] The central control system of the exhibition hall, also known as the central control system, intelligent central control system, intelligent multimedia central control system, etc., is a system that makes exhibition halls, museums, etc. more modern and user-friendly. The central control system of the exhibition hall is a customized integrated system for centralized control of sound, light, electricity and other equipment in the exhibition hall. It is an intelligent central control system that centrally manages and controls all exhibition items in the exhibition hall through intelligent devices such as PC terminals, IPAD tablets, and mobile phones. It is a wireless control panel (usually an IPAD) that controls the host and the wires from the host to various devices, and centrally controls projectors, monitors, air conditioners, lights, power amplifiers, microphones, computers, electric curtains, sliding tracks, electronic screens, etc. in the exhibition hall; when the user touches the button of the corresponding device on the IPAD to operate, the touch screen will send a trigger instruction to the central control host. The central control host receives the instruction and sends a control signal to the corresponding controlled device, and the controlled device will respond to this operation. Each menu on the tablet can control a system, and the entire control interface has an on / off key and a separate control key for each device. With the central control system of the exhibition hall, all operations can be achieved through the tablet. There is no need to walk to each device to operate. As long as the tablet is connected to the network, the management and control of the exhibition hall can be realized.

[0004] However, this operation method through the IPAD often requires the administrator to operate on-site in person to achieve the best exhibition effect in the exhibition hall. However, the number of IPADs that can be operated is limited, and the people who can operate the IPAD also need to be authorized. When the IPAD is not well managed, it is easy to be misoperated by people without permission, resulting in a poor exhibition effect in the venue exhibition hall.

[0005] Therefore, there is an urgent need for an intelligent central control method for a multimedia system in a smart venue, which determines whether a user has the control authority through face recognition and controls the multimedia system of the venue by recognizing the postures of users with control authority. Summary of the Invention

[0006] The object of the present invention is to provide an intelligent central control method for a multimedia system in a smart venue. By collecting three-dimensional point clouds of human bodies in a specified area, identifying human postures or gestures to match user instructions to control the corresponding multimedia system, it solves the problems that existing venue intelligent control devices are easily misoperated and it is impossible to control the multimedia on-site.

[0007] To solve the above technical problems, the present invention is realized through the following technical solutions: The present invention is an intelligent central control method for a multimedia system in a smart venue, including the following steps: Step S1: Deploy the multimedia system in the smart venue, set up a collection area in front of each multimedia device, and scan the human bodies in the area through a depth camera; Step S2: The control host is built with a face database and a human posture instruction library, and trains a face recognition model and a human posture recognition network; Step S3: The depth camera identifies the face of the human body entering the area, determines whether the user has the operation permission for the multimedia system. If there is no operation permission, the operation is terminated; if there is, step S4 is executed; Step S4: The depth camera scans the human body, and the human posture recognition network registers the surface point cloud data of the human body; Step S5: The human posture recognition network recognizes the current human posture and matches the human posture instruction library to obtain the current user instruction; Step S6: The control host sends the successfully matched user instruction to the corresponding multimedia system for control.

[0008] As a preferred technical solution, the multimedia system in the smart venue includes a control host and a projector, a display, an air conditioner, lights, an amplifier, a microphone, a computer, an electric curtain, a sliding track, and an electronic screen that are electrically connected to the control host respectively.

[0009] As a preferred technical solution, in step S2, the training process of the face recognition model is as follows: Step S21: Collect the faces of the employees in the smart venue from different angles; Step S22: Label the identity information, attributes, and key points for each face image; Step S23: Perform operations such as rotation, scaling, flipping, cropping, color transformation, and adding noise to the face images; Step S24: Select a convolutional neural network model, randomly initialize the weight and bias parameters of the model, or use a pre-trained model for transfer learning; Step S25: Select the stochastic gradient descent algorithm to update the model parameters; Step S26: Select cross-entropy loss for the classification task to measure the difference between the model's prediction result and the true label.

[0010] As a preferred technical solution, in step S2, the training process of the human pose recognition network is as follows: Step T21: Obtain the point cloud data of the human body through a 3D human body scanning device, or perform 3D reconstruction of the human body from videos or images to obtain point cloud data; Step T22: Construct triangles in the point set given by the human body point cloud data so that no other points are included in the circumcircle of each triangle, thereby converting the point cloud into a triangular mesh model; Step T23: Process the generated triangular mesh to optimize the vertex coordinates of the triangular mesh; Step T24: Define a loss function and use the MSE loss function to calculate the difference between the predicted vertex coordinates and the true vertex coordinates; Step T25: Input the training set data into the network model, calculate the output of the model through forward propagation, then calculate the loss value according to the loss function, calculate the gradient of the model parameters through the backpropagation algorithm, and finally update the model parameters using an optimization algorithm; Step T26: Repeat the process of step T25 until the preset number of training rounds is reached or the loss value converges.

[0011] As a preferred technical solution, in step T23, the specific process of processing the generated triangular mesh is as follows: Step T231: First construct a super triangle that contains all data points and put it into the triangle list; Step T232: Add the scattered points in the data points one by one in sequence, find the triangle where the point is located, connect the point and the vertices of the triangle where it is located to form three small triangles; Step T233: Calculate the circumcircle of each small triangle. If no other points are included in the circumcircle, insert a new vertex; if other vertices are included in a certain small triangle, swap the diagonals to form a new triangle, and check whether other points are included in the new triangle until all meet the condition of an empty circumcircle; Step T234: Repeat the above step T232 until all data points are inserted, and finally delete the triangle associated with the super triangle.

[0012] As a preferred technical solution, in step S4, when the depth camera scans the human body to collect human body point cloud data, splicing is required; during splicing, the point cloud data collected by different depth cameras needs to be converted to the same coordinate system. The specific operation is as follows: Assume that there is a common point P in two different point cloud data, and the coordinate of this P point in the A point cloud data is , the coordinates in the B point cloud data are . If the point coordinates in the B point cloud data are converted into A, then the B point cloud data needs to be scaled and rotated. The specific formulas for scaling and rotation are as follows: ; In the formula, are the translation parameters in three directions, are the rotation parameters. The formulas for scaling and rotation can be represented by an orthogonal matrix as: .

[0013] As a preferred technical solution, in step S4, when the human body pose recognition network registers the human body surface point cloud data, manually select the area where the feature points are located, divide the point cloud in the selected area of the feature points into 3 planes, fit the plane model parameters, obtain the feature points by the least square method, divide different feature point areas, and then perform hole repair surface reconstruction, feature description, extraction and clustering.

[0014] As a preferred technical solution, the clustering is used to combine the discrete point clouds belonging to each feature pose to form corresponding entities to judge the contour of each pose. The specific clustering formula is as follows: Step S41: Set the initial adaptive K value to 2 and set the DBI index threshold; Step S42: Given a set of data points , each point has a dimension of C; Step S43: Randomly select K points as the initial clustering centers; Step S44: Calculate the Euclidean distance from the remaining points to the clustering centers, and assign the clustering centers according to the distance. The judgment condition is that the sum of the distances from each point to its respective class center is the smallest, that is , in the formula, represents the centroid of each class center, represents the distance from the data point x to the centroid; Step S45: Update the clustering centers and perform iterative solution until the algorithm converges; Step S46: Calculate the DBI index and perform iterative solution by comparing it with the threshold.

[0015] As a preferred technical solution, the calculation formula of the DBI index is as follows: ; In the formula, represents the average distance from the variables in class i to the center of this class, represents the distance between class i and class j, and N is the number of clusters, It means to only calculate the maximum similarity value between class i and other classes.

[0016] The present invention has the following beneficial effects: The present invention collects three-dimensional point clouds of the human body in a specified area, registers the point cloud data on the human body surface, and uses a human pose recognition network to recognize the current pose or gesture of the human body to match user instructions to control the corresponding multimedia system, improving the intelligence and control efficiency of the smart venue.

[0017] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for describing the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a flowchart of an intelligent central control method for a multimedia system in a smart venue according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0021] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0022] In order to make the purpose, technical solutions and advantages of the present application clearer, the following will further elaborate on the present application Figure 1 with reference to the accompanying

[0023] Please refer to Figure 1 As shown, the present invention is an intelligent central control method for a multimedia system in a smart venue, including the following steps: Step S1: Deploy the multimedia system in the smart venue, set a collection area in front of each multimedia device, and scan the human body in the area through a depth camera; Step S2: The control host is built with a face database and a human body posture instruction library, and a face recognition model and a human body posture recognition network are trained; Step S3: The depth camera recognizes the face of the human body entering the area to determine whether the user has the operation permission for the multimedia system. If there is no operation permission, the operation is terminated; if there is, step S4 is executed; Step S4: The depth camera scans the human body, and the human body surface point cloud data is registered by the human body posture recognition network; Step S5: The human body posture recognition network recognizes the current human body posture and matches it with the human body posture instruction library to obtain the current user instruction; Step S6: The control host sends the successfully matched user instruction to the corresponding multimedia system for control.

[0024] The multimedia system of the intelligent venue includes a control host and a projector, a display, an air conditioner, lights, an amplifier, a microphone, a computer, an electric curtain, a sliding track, and an electronic screen that are electrically connected to the control host respectively.

[0025] In step S2, the training process of the face recognition model is as follows: Step S21: Collect the faces of the employees in the intelligent venue from different angles; Step S22: Mark the identity information, attributes, key points, etc. for each face image. Tools such as LabelImg or manual marking can be used; Step S23: Perform operations such as rotation, scaling, flipping, cropping, color transformation, and adding noise to the face images to increase data diversity and scale and improve the generalization ability of the model, but the enhancement degree should be controlled to avoid overfitting; Step S24: Select a convolutional neural network model, randomly initialize the weight and bias parameters of the model, or use a pre-trained model for transfer learning; the convolutional neural network can be VGGNet, ResNet, MobileNet, etc., which are suitable for image classification and feature extraction and can automatically learn the feature representation of face images; or use an adversarial network. The adversarial network consists of a generator and a discriminator and can be used to generate realistic face images for data augmentation or generative adversarial training; According to the selected model, randomly initialize parameters such as the weight and bias of the model, or use a pre-trained model for transfer learning to accelerate the training convergence speed and improve the model performance.

[0026] When selecting the training strategy: determine the batch size, learning rate, number of training epochs, etc. Generally, the batch size is selected between 32 and 128; the initial value of the learning rate can be tried between 0.001 and 0.1 and then adjusted according to the model convergence situation; the number of training epochs may require dozens to hundreds of epochs; Step S25: Select the stochastic gradient descent algorithm to update the model parameters and improve the convergence speed and performance of the model; Step S26: Select cross-entropy loss for classification tasks to measure the difference between the model prediction results and the true labels. Triplet loss focuses on learning the similarity relationship between face images and is commonly used for feature extraction in face recognition. Center loss can make the features of the same category more concentrated.

[0027] Use methods such as L1 regularization, L2 regularization, and Dropout to prevent overfitting. L2 regularization restricts the size of model parameters by adding the sum of squares of model parameters to the loss function. Dropout randomly discards a part of neurons during training to reduce the dependence between neurons. Monitor the performance of the model on the validation set and terminate training early when the performance no longer improves to avoid overfitting.

[0028] Deploy the trained model to the control host, which may involve integrating the model into a security monitoring system, a mobile application, or other platforms. During the deployment process, consider the real-time performance, accuracy, and security of the model. Then, continuously optimize and update the model according to the feedback and new data in the actual application to improve the performance and adaptability of the model.

[0029] In step S2, the training process of the human pose recognition network is as follows: Step T21: Obtain the point cloud data of the human body through a 3D human body scanning device, or perform 3D reconstruction of the human body from videos or images to obtain point cloud data; Step T22: Construct triangles in the point set given by the human body point cloud data so that no other points are contained within the circumcircle of each triangle, thereby converting the point cloud into a triangular mesh model to ensure the quality and stability of the triangular mesh; Step T23: Process the generated triangular mesh to optimize the vertex coordinates of the triangular mesh, such as performing Laplacian smoothing, mean filtering, etc. to smooth the outermost triangular mesh; Step T24: Define the loss function and use the MSE loss function to calculate the difference between the predicted vertex coordinates and the true vertex coordinates; Step T25: Input the training set data into the network model, calculate the output of the model through forward propagation, then calculate the loss value according to the loss function, calculate the gradient of the model parameters through the backpropagation algorithm, and finally use the optimization algorithm to update the model parameters; Step T26: Repeat the process of step T25 until the preset number of training epochs or the loss value converges.

[0030] In step T23, the specific process of processing the generated triangular mesh is as follows: Step T231: First, construct a super triangle that contains all data points and put it into the triangle list; Step T232: Add the scattered points in the data points one by one in sequence, find the triangle where the point is located, connect the point with the vertices of the triangle where it is located to form three small triangles; Step T233: Calculate the circumcircle of each small triangle. If no other points are included in the circumcircle, insert a new vertex; if other vertices are included in a certain small triangle, swap the diagonals to form a new triangle, and check whether other points are included in the new triangle until all meet the condition of an empty circumcircle; Step T234: Repeat the above Step T232 until all data points are inserted, and finally delete the triangles associated with the super triangle.

[0031] In Step S4, because when the depth camera collects point cloud data, due to the occlusion of the measured target itself or other objects and the perspective limitation of the depth camera itself, using 1 depth camera cannot obtain the full view of the human body of the measured building, and it is necessary to obtain point cloud data from different depth camera perspectives; therefore, when the depth camera scans the human body to collect human body point cloud data, splicing is required; when splicing, it is necessary to convert the point cloud data collected by different depth cameras to the same coordinate system. The specific operations are as follows: Assume that there is a common point P in two different point cloud data. The coordinate of this P point in the A point cloud data is , and the coordinate in the B point cloud data is . To convert the point coordinates in the B point cloud data to A, then scale and rotate the B point cloud data. The specific formulas for scaling and rotation are as follows: ; In the formula, are the translation parameters in three directions, are the rotation parameters. The formulas for scaling and rotation can be represented by an orthogonal matrix as: .

[0032] In Step S4, when the human body pose recognition network registers the human body surface point cloud data, manually select the area where the feature points are located, divide the point cloud in the selected area of the feature points into 3 planes, fit the plane model parameters, obtain the feature points by the least squares method, divide different feature point areas, and then perform hole repair and surface reconstruction, and perform feature description, extraction and clustering.

[0033] Clustering is used to combine the discrete point clouds belonging to each feature pose to form corresponding entities to judge the contour of each pose. The specific clustering formula is as follows: Step S41: Assume that the initialized adaptive K value is 2 and set the DBI index threshold; Step S42: Given a set of data points , each point having a dimension of C; Step S43: Randomly select K points as the initial cluster centers; Step S44: Calculate the Euclidean distances from the remaining points to the cluster centers, and assign cluster centers according to the distances. The judgment condition is that the sum of the distances d from each point to its respective class center is the smallest, and , where, represents the centroid of each class center, represents the distance from the data point x to the centroid; Step S45: Update the cluster centers and perform iterative solution until the algorithm converges; Step S46: Calculate the DBI index, and perform iterative solution by comparing it with the threshold.

[0034] The calculation formula of the DBI index is as follows: ; where, represents the average distance from the variables in class i to the class center, represents the distance between class i and class j, N is the number of clusters, represents calculating only the maximum similarity value between class i and other classes; The main idea of the index is to calculate the ratio of the sum of the intra-class distances to the inter-class distances. By introducing this quantitative index, the quality of the clustering effect corresponding to the K value can be determined, and the optimal K value corresponding to the best clustering can be obtained through iteration. The smaller the value of DBI, the better the clustering effect. However, to avoid getting stuck in an infinite loop of solving the K value and the corresponding index, a threshold is adopted to limit the number of clusters, that is, to select the K value corresponding to the minimum index among the finite number of K values as the optimal clustering parameter. By presetting the threshold , the optimal K value corresponding to the optimal clustering can be adaptively solved, reducing the error caused by manually selecting the clustering number K value, improving the clustering efficiency, and achieving adaptive clustering to a certain extent. Clustering the targets according to the distribution of points is convenient for feature extraction, can adapt to complex and changeable situations, and reduce the setting of manual parameters.

[0035] It should be noted that in the above system embodiments, the included units are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0036] In addition, those of ordinary skill in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the corresponding program can be stored in a computer-readable storage medium.

[0037] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An intelligent central control method for a smart venue multimedia system, characterized in that: The steps include: Step S1: Deploy the multimedia system in the smart venue, set up a collection area in front of each multimedia, and scan the human body in the area through a depth camera; Step S2: The control host has a built-in face database and a human posture instruction library, and trains a face recognition model and a human posture recognition network; Step S3: The depth camera recognizes the face of the person entering the area and determines whether the user has the operating authority of the multimedia system. If not, the operation is terminated; if yes, step S4 is executed; Step S4: The depth camera scans the human body, and the human body posture recognition network performs registration on the human body surface point cloud data; Step S5: the human posture recognition network recognizes the current human posture and matches the human posture instruction library to obtain the current user instruction; Step S6: The control host sends the successfully matched user instructions to the corresponding multimedia system for control.

2. According to claim 1, the intelligent central control method of a smart venue multimedia system is characterized in that: The multimedia system of the smart venue includes a control host and a projector, a display, an air conditioner, a light, a power amplifier, a microphone, a computer, an electric curtain, a sliding track and an electronic screen which are electrically connected to the control host.

3. The intelligent central control method of a smart venue multimedia system according to claim 1 is characterized in that: In step S2, the face recognition model training process is as follows: Step S21: collecting facial images of employees at the smart venue from different angles; Step S22: labeling identity information, attributes and key points for each face image; Step S23: rotating, scaling, flipping, cropping, color changing and adding noise to the face image; Step S24: Select a convolutional neural network model, randomly initialize the weights and bias parameters of the model, or use a pre-trained model for transfer learning; Step S25: Select the stochastic gradient descent algorithm to update the model parameters; Step S26: Select cross entropy loss for classification tasks to measure the difference between the model prediction results and the true labels.

4. The intelligent central control method of a smart venue multimedia system according to claim 1 is characterized in that: In step S2, the training process of the human posture recognition network is as follows: Step T21: obtaining point cloud data of the human body through a 3D human body scanning device, or performing three-dimensional reconstruction of the human body from a video or image to obtain point cloud data; Step T22: construct triangles in the point set given by the human body point cloud data, so that the circumscribed circle of each triangle does not contain other points, thereby converting the point cloud into a triangular mesh model; Step T23: Processing the generated triangular mesh to optimize the vertex coordinates of the triangular mesh; Step T24: define a loss function and use the MSE loss function to calculate the difference between the predicted vertex coordinates and the actual vertex coordinates; Step T25: Input the training set data into the network model, calculate the output of the model through forward propagation, then calculate the loss value according to the loss function, then calculate the gradient of the model parameters through the back propagation algorithm, and finally use the optimization algorithm to update the model parameters; Step T26: Repeat the process of step T25 until the preset number of training rounds is reached or the loss value converges.

5. The intelligent central control method of a smart venue multimedia system according to claim 4 is characterized in that: In step T23, the specific process of processing the generated triangular mesh is as follows: Step T231: first construct a super triangle containing all data points and put it into the triangle list; Step T232: add the scattered points in the data points one by one, find the triangle where the point is located, and connect the point and the vertex of the triangle where it is located to form three small triangles; Step T233: Calculate the circumscribed circle of each small triangle. If the circumscribed circle does not contain other points, insert new vertices. If a small triangle contains other vertices, swap the diagonals to form a new triangle. Check whether the new triangle contains other points until all meet the empty circumscribed circle condition. Step T234: Repeat the above step T232 until all data points are inserted, and finally delete the triangles associated with the super triangle.

6. The intelligent central control method of a smart venue multimedia system according to claim 1 is characterized in that: In step S4, the depth camera scans the human body to collect human point cloud data. When stitching, the point cloud data collected by cameras at different depths need to be converted into a unified coordinate system. The specific operation is as follows: Assume that there is a common point P in the two different point cloud data. The coordinates of point P in point cloud data A are , the coordinates in the point cloud data of B are , convert the point coordinates in the B point cloud data to A, and then scale and rotate the B point cloud data. The specific scaling and rotation formulas are as follows: ； In the formula, are the translation parameters in three directions, is the rotation parameter, the formula for scaling and rotation can be expressed by an orthogonal matrix: 。 7. The intelligent central control method of a smart venue multimedia system according to claim 1 is characterized in that: In step S4, when the human posture recognition network aligns the human body surface point cloud data, the area where the feature points are located is manually selected, the point cloud in the area where the selected feature points are located is divided into three planes, and the plane model parameters are fitted. The feature points are obtained by the least squares method, the different feature point areas are segmented, and then the hole repair surface is reconstructed to describe, extract and cluster the features.

8. The intelligent central control method of a smart venue multimedia system according to claim 7 is characterized in that: The clustering is used to combine the discrete point clouds belonging to each characteristic posture to form a corresponding entity to determine the outline of each posture. The specific clustering formula is as follows: Step S41: set the initial adaptive K value to 2 and set the DBI index threshold; Step S42: Given a set of data points , each point dimension is C; Step S43: randomly select K points as initial cluster centers; Step S44: Calculate the Euclidean distance from the remaining points to the cluster center, and assign the cluster center according to the distance. The judgment condition is that the sum of the distance from each point to the respective cluster center and d is the smallest, and , where represents the centroid of each type of center, Represents the distance from the data point x to the centroid; Step S45: updating the cluster center and solving iteratively until the algorithm converges; Step S46: Calculate the DBI index, compare it with the threshold and perform iterative solution.

9. The intelligent central control method of a smart venue multimedia system according to claim 8, characterized in that: The calculation formula of the DBI index is as follows: ； In the formula, represents the average distance from the variable in class i to the center of the class, represents the distance between class i and class j, N is the number of clusters, Indicates that only the maximum similarity value between class i and other classes is calculated.

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