Technical and tactical ability monitoring and analysis system and method for tennis-related sports

Abstract

Disclosed are a system and method for monitoring and analyzing technical and tactical capabilities for tennis sports. The system includes a camera parameter estimation module for receiving multiple video frames of a sport captured from multiple perspectives by multiple time-synchronized cameras, and calculating camera parameters using a neural network model based on positional information of elements associated with the court in the multiple video frames; a ball positioning module for determining the two-dimensional position of the ball from the multiple video frames using a deep neural network model, and determining the three-dimensional position of the ball based on the determined two-dimensional position of the ball and camera parameters; and a player posture detection module for determining the two-dimensional posture of all detectable human targets from the multiple video frames, determining the three-dimensional posture of the human targets based on the determined two-dimensional posture of the human targets and camera parameters, and determining the three-dimensional posture of the player based on the three-dimensional posture of the human targets and three-dimensional spatial geometric relationship constraints associated with the court.

Description

Technical Field

[0001] The present invention relates to monitoring and analysis of sports technical and tactical capabilities. Background Art

[0002] Tennis-based sports (such as table tennis, badminton, tennis, and volleyball) are widely popular. These sports, characterized by high ball speeds, place high demands on players' technical and tactical abilities, as well as their ability to react quickly to game situations. At high-level competitions, victory or defeat often hinges on subtle technical and tactical differences. Therefore, accurately monitoring and analyzing players' technical and tactical abilities during training and competition is crucial for improving training efficiency and competitive performance.

[0003] Traditional training and match analysis methods for tennis rely on manual observation by coaches or analysts. This method is highly subjective, inefficient, and difficult to implement large-scale and in-depth data analysis.

[0004] Computer vision, which enables computers to accurately identify and track objects by analyzing images and videos, has also seen widespread adoption in sports analysis, providing an efficient and objective means of monitoring and analyzing athlete performance.

[0005] Chinese patent application publication CN 109977752 A discloses a method for analyzing badminton player skills and tactics based on sequence pattern mining, which requires manual video viewing and action annotation, making it difficult to automatically process massive amounts of data.

[0006] Chinese patent application publication CN 110433471 A discloses a system and method for monitoring and analyzing badminton trajectory. This system utilizes a single high-speed depth camera, requiring specialized equipment and requiring a relatively demanding setup. Furthermore, this method has limited ability to handle occlusions, resulting in low accuracy in obtaining spatial information about the badminton ball and failing to estimate the spatial information of the player.

[0007] Chinese patent application publication CN 116958872 A discloses an intelligent auxiliary training method and system for badminton. The method uses a binocular camera, which has limited ability to process occlusion and calculate depth information, and has low accuracy in obtaining spatial information of the badminton and the player.

[0008] There is a need in the art for more accurate and efficient monitoring and analysis of tennis sports. Summary of the Invention

[0009] The present invention is provided in order to provide a more accurate and efficient monitoring and analysis technology for tennis sports.

[0010] According to one aspect of the present invention, a technical and tactical capability monitoring system for tennis sports is provided, which includes: a camera parameter estimation module for: receiving multiple video frames of movement captured by multiple time-synchronized cameras at multiple perspectives; using a neural network model to calculate camera parameters based on position information of elements associated with a court in the multiple video frames; a ball positioning module for: using a deep neural network model to determine the two-dimensional position of the ball from the multiple video frames; determining the three-dimensional position of the ball based on the determined two-dimensional position of the ball and the camera parameters; and a player posture detection module for: determining the two-dimensional posture of all detectable human targets from the multiple video frames; determining the three-dimensional posture of the human target based on the determined two-dimensional posture of the human target and the camera parameters; and determining the three-dimensional posture of the player through the three-dimensional posture of the human target based on the three-dimensional spatial geometric relationship constraints associated with the court.

[0011] According to another aspect of the present invention, a technical and tactical ability analysis system for tennis sports is provided, which includes: the technical and tactical ability monitoring system as described above; and a technical and tactical analysis module for determining technical and tactical data based on the determined three-dimensional position of the ball and / or the three-dimensional posture of the player.

[0012] According to another aspect of the present invention, a system for presenting a three-dimensional animation of a tennis sport is provided, comprising: the technical and tactical ability monitoring system as described above; and a three-dimensional rendering module for: receiving data indicating the three-dimensional position of a ball and / or the three-dimensional posture of a player determined within a time period; and rendering the received data into a three-dimensional animation.

[0013] According to another aspect of the present invention, a television broadcasting system for tennis sports is provided, which includes: a technical and tactical ability monitoring system as described above; a technical and tactical analysis module for determining technical and tactical data based on the determined three-dimensional position of the ball and / or the three-dimensional posture of the player; a three-dimensional rendering module for: receiving data indicating the three-dimensional position of the ball and / or the three-dimensional posture of the player determined within a time period; and rendering the received data into a three-dimensional animation and / or visual rendering of the determined technical and tactical data to obtain a rendered video; an online packaging module for beautifying and packaging the technical and tactical data to obtain a key signal; a super slow motion system for recording and editing the rendered video to generate a super slow motion picture; a switcher for integrating and processing the key signal and the super slow motion picture to generate a broadcast picture; and a television signal broadcasting system for broadcasting television signals to television viewers based on the broadcast picture.

[0014] According to another aspect of the present invention, a method for monitoring technical and tactical capabilities of tennis sports is provided, comprising: receiving multiple video frames of motion captured by multiple time-synchronized cameras at multiple perspectives; calculating camera parameters based on position information of elements associated with a court in the multiple video frames using a neural network model; determining the two-dimensional position of a ball from the multiple video frames using a deep neural network model; determining the three-dimensional position of the ball based on the determined two-dimensional position of the ball and the camera parameters; determining the two-dimensional poses of all detectable human targets from the multiple video frames; determining the three-dimensional poses of the human targets based on the determined two-dimensional poses of the human targets and the camera parameters; and determining the three-dimensional poses of the players through the three-dimensional poses of the human targets based on three-dimensional spatial geometric relationship constraints associated with the court.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, on which computer instructions are stored. When the computer instructions are executed by a processor, the steps of the method described above are implemented.

[0016] According to another aspect of the present invention, a computer program product is provided, comprising computer instructions, which implement the steps of the method described above when executed by a processor.

[0017] The system and method according to the embodiments of the present invention make the monitoring and analysis of tennis games more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Embodiments of the present invention are described with reference to the accompanying drawings.

[0019] Figure 1 A block diagram of a system for monitoring technical and tactical abilities in tennis according to some embodiments of the present invention is shown.

[0020] Figure 2 A block diagram of a system for analyzing technical and tactical capabilities of tennis sports according to some embodiments of the present invention is shown.

[0021] Figure 3 A block diagram illustrating a system for presenting three-dimensional animation of tennis motions according to some embodiments of the present invention.

[0022] Figure 4 Example animation frames presented by a system for presenting three-dimensional animation of tennis motion according to some embodiments of the present invention are shown.

[0023] Figure 5 An overall flow chart of a system for presenting three-dimensional animation of tennis motion according to some embodiments of the present invention is shown.

[0024] Figure 6A block diagram illustrating a system for televising tennis sports according to some embodiments of the present invention is shown.

[0025] Figure 7 A schematic diagram illustrating the arrangement of video acquisition cameras for a television broadcasting system for tennis according to some embodiments of the present invention is provided.

[0026] Figure 8 A flow chart illustrating a method for monitoring technical and tactical abilities in tennis sports according to some embodiments of the present invention is shown.

[0027] Figure 9 A flow chart illustrating a process of calculating camera parameters according to some embodiments of the present invention.

[0028] Figure 10 A flowchart illustrating a process of training a neural network model for calculating camera parameters according to some embodiments of the present invention.

[0029] Figure 11 A flow chart illustrating a process for determining the two-dimensional position of a ball according to some embodiments of the present invention.

[0030] Figure 12 A flow chart illustrating a process for determining a player's three-dimensional pose according to some embodiments of the present invention.

[0031] Figure 13 A block diagram illustrating a machine-readable storage medium according to some embodiments of the present invention.

[0032] Figure 14 A block diagram illustrating a computer program product according to some embodiments of the invention. DETAILED DESCRIPTION

[0033] As used herein, the term "netball sports" refers to a type of sport that generally involves two teams competing against each other on a playing field divided by a net, with each team competing with a ball in their respective field, and with players on each team avoiding direct physical contact or confrontation with the opposing team. Examples of netball sports include table tennis, badminton, tennis, volleyball, and the like.

[0034] According to one aspect of the present invention, a system for monitoring technical and tactical capabilities of tennis sports is provided.

[0035] Figure 1 A block diagram of a system 100 for monitoring technical and tactical abilities in tennis according to some embodiments of the present invention is shown.

[0036] The technical and tactical ability monitoring system 100 may include a camera parameter estimation module 110, a ball positioning module 120, and a player gesture detection module 130. In some embodiments, the technical and tactical ability monitoring system 100 may include a computing device (e.g., a server), which may include various computing resources (e.g., a processor, etc.). Each of the camera parameter estimation module 110, the ball positioning module 120, and the player gesture detection module 130 may include software components and / or hardware components. For example, each of the camera parameter estimation module 110, the ball positioning module 120, and the player gesture detection module 130 may be programmed to call corresponding computing resources to perform designated operations.

[0037] The camera parameter estimation module 110 may be configured to receive a plurality of video frames 140. The plurality of video frames 140 may be a plurality of video frames of motion captured by a plurality of time-synchronized cameras at a plurality of viewing angles. Figure 7 The scene arrangement for capturing a plurality of video frames 140 is described. The camera parameter estimation module 110 may also be configured to calculate camera parameters using a neural network based on position information of features associated with the court in the plurality of video frames 140. For example, features associated with the court may include various lines on the court, such as boundary lines, lines defining relevant areas, etc. The camera parameters may include extrinsic parameters of the camera.

[0038] Ball positioning module 120 can be used to determine the two-dimensional position of the ball from multiple video frames 140 using a deep neural network model. This deep neural network model is trained to accurately capture the two-dimensional coordinate information of the ball from a series of consecutive video frames, laying a solid foundation for subsequent three-dimensional positioning, motion assessment, and analysis. Ball positioning module 120 can also be used to determine the three-dimensional position of the ball 150 based on the determined two-dimensional position of the ball and camera parameters.

[0039] The player pose detection module 130 may be configured to determine the 2D poses of all detectable human targets from a plurality of video frames. This may be achieved, for example, by executing a target detection algorithm. The player pose detection module 130 may also be configured to determine the 3D pose of the human targets based on the determined 2D poses of the human targets and camera parameters. The player pose detection module 130 may also be configured to determine the 3D pose 160 of the player from the 3D poses of the human targets based on 3D spatial geometric constraints associated with the court. For example, the 3D spatial geometric constraints may include a 3D size constraint for the court.

[0040] The three-dimensional position 150 of the ball determined by the ball positioning module 120 and the three-dimensional posture 160 of the player determined by the player posture detection module 130 can be used as outputs of the technical and tactical ability monitoring system 100, as monitoring indicators of the player's tactics and technical performance during tennis sports. They can be used as a reference for coaches, analysts, and players, and as a basis for further adjusting training and competition plans to improve training and competition levels.

[0041] According to some embodiments of the present invention, the technical and tactical ability monitoring system 100 can effectively alleviate the occlusion problem by performing visual processing on video frames collected from multiple perspectives during tennis sports, and the results are highly accurate. In addition, the technical and tactical ability monitoring system 100 can also provide the three-dimensional position of the ball and the three-dimensional posture of the player, thereby providing a solid foundation for further in-depth and extensive technical and tactical analysis.

[0042] In some embodiments, to calculate camera parameters, the camera estimation module 110 may be configured to detect a plurality of key points on the court in the plurality of video frames 140. For example, the key points may include points on lines on the court, intersections of lines, and the like. For example, 100 key points may be detected. Alternatively, more or fewer key points may be detected depending on the actual sports scenario and accuracy requirements.

[0043] The camera estimation module 110 can also be configured to match multiple key points with a predefined standard court template. For example, the multiple key points can be densely matched with corresponding points on the standard court template. The camera estimation module 110 can also be configured to estimate camera parameters using an algorithm based on the matching results. For example, the algorithm can include a PnP algorithm, which is a method for solving 3D to 2D point mapping problems. For example, the camera parameters can include camera extrinsic parameters.

[0044] In some embodiments, the camera estimation module 110 can be configured to estimate camera parameters based on the homography matching error between the homography transformation of multiple key points and the homography transformation of a standard 3D court template. In some embodiments, the detected key points can cover the entire court. Since the homography matching error for each pixel of the court increases with the distance between the pixel and the key point of the generated homography transformation in each video frame, ensuring that the detected key points cover the entire court helps reduce the spacing between points. This allows the homography matching error caused by occluded key points to be localized and gradually eliminated as the pixel approaches other key points, thereby reducing the error caused by occlusion. Alternatively or additionally, the detected key points can be evenly distributed across the court. By setting up evenly distributed key points, the homography matching error can be further evenly limited to each local area, thereby improving overall accuracy.

[0045] In order to train the neural network model to perform the above-mentioned process of calculating camera parameters, it is necessary to provide ground truth during the training process, which involves manually annotating a corresponding number (e.g., 100) of key points for the scene in each video frame. In order to save a lot of manual annotation work, in some embodiments, a template-aware annotation tool can be used. With this annotation tool, only a threshold number of key points need to be manually annotated. As an example, the threshold number can be less than (e.g., much less than) the number of key points detected. As an example, the threshold number can be 4-6, which is much less than the number of key points detected by the neural network model during the inference process (e.g., 100).

[0046] Accordingly, the neural network model can be trained by the following steps: arbitrarily manually annotating a threshold number of key points in each video frame, where the threshold number is less than the number of key points detected; calculating the homography transformation between the court in the video frame and the standard three-dimensional court template for the threshold number of key points; based on the result of the homography transformation, mapping the intersection points of the court lines associated with the court from the standard three-dimensional court template to the video frame; comparing the mapped intersection points with the corresponding intersection points in the standard three-dimensional court template; manually fine-tuning the result of the comparison to indicate the position of one or more intersection points with the maximum error in the video frame; and adjusting the homography transformation based on the position of the fine-tuned one or more intersection points by minimizing the remapping error method.

[0047] As an example, the above process can first involve manually calibrating any four visible keypoints on the court in the video frame. Based on these four keypoints, a homography is calculated between the court in the video frame and a standard 3D template court. Then, based on the result of this homography, the intersection points (e.g., all intersection points) of the court lines from the standard 3D court template are mapped onto the video frame, and the mapped intersection points are compared with the corresponding intersection points on the court in the video frame. The positions of one or more intersection points with the largest error relative to the actual court intersections can then be manually fine-tuned in the video frame. These fine-tuned intersection points generate new corresponding point relationships. Since no homography can typically accurately match a threshold number of keypoints (e.g., four), a remapping error minimization method is employed to calculate the required homography. The resulting trained neural network model can extract accurate 2D-3D point pairs from the accurate homography, allowing algorithms (e.g., PnP algorithms) to be used to calculate camera parameters (e.g., camera extrinsics).

[0048] In some embodiments, considering the diversity of video sources in practical application scenarios, the ball positioning module 120 may perform preprocessing. For example, the ball positioning module 120 may adjust the resolution of all video frames to a fixed predetermined resolution. As an example, this fixed predetermined resolution may be 288x512 pixels. The two-dimensional position of the ball is subsequently determined based on the video frames with adjusted resolution. This ensures the universal applicability of the deep neural network to video frames of different resolutions while effectively controlling the consumption of computing resources, thereby ensuring efficient execution of the algorithm.

[0049] In some embodiments, to further enhance the concept of time series analysis, the ball positioning module 120 may receive multiple consecutive video frames (e.g., eight consecutive frames) as input to the deep neural network. This not only enhances the tracking consistency and stability of the deep neural network model in the temporal dimension, but also enables the deep neural network model to more delicately analyze the continuity and changing trends of motion.

[0050] In some embodiments, background frame correction technology can be introduced to combat complex and changing background interference and improve ball recognition accuracy. The ball positioning module 120 can independently calculate the background of video frames from the same perspective and overlay the calculated results onto the video frames. For example, the background of a video frame can be obtained by capturing a scene without players on the court, or by capturing multiple frames of the court with players and algorithmically removing the players. This background frame correction technology can significantly enhance the contrast between the ball and the background, thereby enabling clear ball positioning even in complex game environments.

[0051] In some embodiments, the ball positioning module 120 can be used to generate a heat map that indicates the area where the ball is likely to appear and the probability density of its appearance in the area. The generated heat map can be adapted to the size of the input video frame. In order for the ball positioning module 120 to generate a heat map, during the training phase, the real position information of the marked ball can be used to generate an accurate heat map. After training is completed, in order to determine the predicted two-dimensional coordinates of the ball from the heat map, post-processing techniques can be implemented in the inference phase. The ball positioning module 120 can be used to determine the position of the ball from the heat map using a contour extraction method and / or an optimal maximum contour method, thereby achieving extremely high positioning accuracy.

[0052] In some embodiments, to further improve the inference speed of deep learning models in practical applications, the original deep learning models can be converted and optimized. For example, NVIDIA Tensor RT technology can be used to convert and optimize the original PyTorch model, quantizing the model parameters to half-precision (a lower precision than the original precision). This not only verifies a slight decrease in model performance in the test set, but also improves the model's inference speed without sacrificing too much accuracy, making the entire ball positioning process smoother and more immediate.

[0053] In some embodiments, the player pose detection module 130 may identify all detectable human targets using a target detection algorithm. The player pose detection module 130 may utilize a two-dimensional tracking algorithm to generate a sequence of bounding boxes for each human target, crop and magnify the image within the bounding boxes, and employ a two-dimensional pose estimation algorithm to obtain the two-dimensional pose of the human target. The player pose detection module 130 may perform triangulation based on the two-dimensional pose information of the human target in the multi-view video frame coordinate system and camera parameters to determine the three-dimensional pose coordinates of the human target.

[0054] In some embodiments, the player posture detection module 130 may filter out the 3D postures of a predetermined number of players from the 3D postures of all detectable human targets based on the 3D spatial geometric relationship constraints associated with the court, wherein the predetermined number is determined at least in part based on the number of players specified in the sport. As an example, the 3D spatial geometric relationship constraints associated with the court may include the 3D size constraints of the court. Taking badminton as an example, the player posture detection module 130 may filter out the 3D posture information of the 2 (for singles) or 4 (for doubles) players with the highest 3D position matching degree of the badminton court from the 3D postures of all detected human targets. By combining the 3D spatial geometric relationship constraints associated with the court, players can be identified more efficiently and accurately from all detected human targets.

[0055] In some embodiments, the player pose detection module 130 may determine the player's three-dimensional pose using a process different from the traditional top-down "object detection-object tracking-pose recognition" process.

[0056] Chinese patent application publication CN 116958872 A uses a traditional top-down "target detection-target tracking-posture recognition" process. For the task of estimating the two-dimensional pose of a player, the invention adopts a top-down target detection-target tracking-posture recognition process. Specifically, a target detection algorithm is first used to obtain bounding boxes for all identifiable human targets. These are then filtered using the court's sidelines, retaining the bounding boxes corresponding to the players. A two-dimensional tracking algorithm is then used to obtain a sequence of bounding boxes corresponding to each player. The images within the bounding boxes are cropped and magnified, and a two-dimensional pose estimation algorithm is used to obtain the player's two-dimensional pose. Subsequently, triangulation is performed based on the two-dimensional information of the badminton and player in the monocular or binocular image coordinate system and the camera parameters to obtain the three-dimensional positions of the badminton and player. Finally, continuous images or videos are used to compose the motion trajectories of the badminton and player.

[0057] In contrast, in some embodiments, a highly efficient 3D tracking detection mode can be employed. The player pose detection module 130 can estimate the positional range of each tracked player across multiple video frames at the next moment, based on the player's 3D pose and camera parameters, and utilizing the continuity of 3D pose changes over adjacent time periods, using back-projection techniques. Within this positional range, the player pose detection module 130 can skip target detection and directly estimate the player's 2D pose at the next moment. Thus, by leveraging the characteristic that detected players continuously move within the local image range over adjacent time periods and combining it with the 3D spatial geometric constraints associated with the court (which constrain the number and possible positions of players within a specified area for the sport), target detection is no longer required for the detected player at the next moment. Instead, individual 2D pose estimation and correction are performed based on the player's pose at the current moment, thereby improving the efficiency of generating a continuous time series of player 2D poses.

[0058] In some embodiments, after generating a continuous time series of player 2D poses according to the above process, the player pose detection module 130 can also skip the matching task for the estimated player 2D pose at the next moment and directly perform single-person triangulation on the player. Thus, during the triangulation phase, the newly generated single-person 2D pose estimation results can be directly used to perform single-person triangulation on the player based on the consistency of the projection area of ​​the same player from different perspectives. This avoids the need to match multiple detection results from different perspectives, further improving matching accuracy and significantly reducing matching time.

[0059] According to one aspect of the present invention, a technical and tactical ability analysis system for tennis sports is provided.

[0060] Figure 2A block diagram of a technical and tactical ability analysis system 200 for tennis according to some embodiments of the present invention is shown.

[0061] The technical and tactical capability sharing system 200 may include a camera parameter estimation module 210, a ball positioning module 220, a player posture detection module 230, and a technical and tactical analysis module 240. In some embodiments, the technical and tactical capability monitoring system 200 may include a computing device (e.g., a server), which may include various computing resources (e.g., a processor, etc.). Each of the camera parameter estimation module 210, the ball positioning module 220, the player posture detection module 230, and the technical and tactical analysis module 240 may include software components and / or hardware components. For example, each of the camera parameter estimation module 210, the ball positioning module 220, the player posture detection module 230, and the technical and tactical analysis module 240 may be programmed to call corresponding computing resources to perform designated operations.

[0062] The camera parameter estimation module 210 can be combined with the above Figure 1 The camera parameter estimation module 110 described above is similar. The ball positioning module 220 can be combined with the above Figure 1 The ball positioning module 110 described above is similar. The player posture detection module 230 can be combined with the above Figure 1 The player posture detection module 130 described above is similar. The technical and tactical ability analysis module 200 can receive multiple video frames 250. The multiple video frames 250 can be combined with the above Figure 1 The ball positioning module 220 can determine the three-dimensional position 260 of the ball, which can be combined with the above Figure 1 The three-dimensional position of the ball 150 is similar to that described above. The player posture detection module 230 can determine the three-dimensional posture 270 of the player, which can be combined with the above Figure 1 The three-dimensional pose 160 of the player is described similarly.

[0063] The technical and tactical analysis module 240 may receive the three-dimensional position 260 of the ball and / or the three-dimensional posture 270 of the player to determine technical and tactical data 280 .

[0064] The technical and tactical data that can be determined by the technical and tactical analysis module 240 may include net crossing speed, net crossing height, net crossing angle, number of shots and point statistics, player movement distance, and player jump events.

[0065] For example, the technical and tactical analysis module 240 can calculate the ball's three-dimensional velocity and direction at any given moment based on the ball's three-dimensional position information 260 using the first-order derivative of distance with respect to time. This can be used to calculate the ball's height from the ground at the moment closest to the net, its velocity, and the projection of the angle between the ball's direction of motion and the net plane onto the ground, thereby calculating the ball's speed, height, and angle across the net.

[0066] As an example, the technical and tactical analysis module 240 can determine the occurrence, time, and location of a hitting event by judging whether the angle between the ball speed direction and the normal direction of the net changes, thereby providing statistical analysis data on the number and location of hitting.

[0067] For example, the technical and tactical analysis module 240 may further accumulate the distance traveled by the player's center of gravity within a specified time period based on the player's three-dimensional posture to provide statistical analysis of the player's movement distance. For example, by detecting whether the height of the player's lowest three-dimensional key point (e.g., ankle or toe) above the ground exceeds a preset threshold height (e.g., 25 cm), the occurrence of a jump event can be determined. The maximum height above the ground within the time range of the jump event can then be calculated to determine the jump height.

[0068] As an example, the technical and tactical data 280 that the technical and tactical analysis module 240 can determine and output may include: player's moving distance (meters), maximum smash speed over the net (kilometers / hour), maximum net crossing angle in front of the net (degrees), player's number of jump attacks (times), player's maximum jump height (centimeters), etc.

[0069] According to some embodiments of the present invention, the technical and tactical ability analysis system 200 can determine technical and tactical data 280 based on the determined three-dimensional position 260 of the ball and / or the three-dimensional posture 270 of the player, thereby providing an accurate reference basis for improving the player's training and competition performance.

[0070] According to one aspect of the present invention, a system for presenting three-dimensional animation of tennis sports is provided.

[0071] Figure 3 A block diagram is shown of a system 300 for presenting a three-dimensional animation of tennis motion according to some embodiments of the present invention.

[0072] The system 300 for presenting three-dimensional animation may include a camera parameter estimation module 310, a ball positioning module 320, a player gesture detection module 330, and a three-dimensional rendering module 240. In some embodiments, the technical and tactical ability monitoring system 300 may include a computing device (e.g., a server), which may include various computing resources (e.g., a processor, etc.). Each of the camera parameter estimation module 310, the ball positioning module 320, the player gesture detection module 330, and the three-dimensional rendering module 340 may include software components and / or hardware components. For example, each of the camera parameter estimation module 310, the ball positioning module 320, the player gesture detection module 330, and the three-dimensional rendering module 340 may be programmed to call corresponding computing resources to perform designated operations.

[0073] The camera parameter estimation module 310 can be combined with the above Figure 1 The camera parameter estimation module 110 described above is similar. The ball positioning module 320 can be combined with the above Figure 1 The ball positioning module 120 described above is similar. The player posture detection module 330 can be combined with the above Figure 1 The system 300 for presenting three-dimensional animation may receive a plurality of video frames 350. The plurality of video frames 350 may be combined with the above Figure 1 The ball positioning module 320 can determine the three-dimensional position 360 of the ball, which can be combined with the above Figure 1 The three-dimensional position of the ball 150 is similar to that described above. The player posture detection module 330 can determine the three-dimensional posture 370 of the player, which can be combined with the above Figure 1 The three-dimensional pose 160 of the player is described similarly.

[0074] In some embodiments, the 3D rendering module 340 may be used for animation rendering of real-time games.

[0075] The 3D rendering module 340 may be configured to receive data indicating a 3D position of a ball 360 determined over a period of time. In some embodiments, the 3D rendering module 340 may be configured to receive data indicating a 3D pose 370 of a player determined over a period of time. In some embodiments, the 3D rendering module 340 may be configured to receive data indicating a 3D position of a ball 360 and a 3D pose 370 of a player determined over a period of time.

[0076] The 3D rendering module 340 can also be used to render the received data into a 3D animation 380. Depending on the data received by the 3D rendering module 340, the 3D rendering module 340 can render the received data into a 3D animation of the ball over a period of time, a 3D animation of the players, or a 3D animation reflecting the relationship between the player and the ball.

[0077] In some embodiments, the system 300 for presenting three-dimensional animation may further include a technique and tactics analysis module 390. The technique and tactics analysis module 390 may be combined with the above Figure 2 The tactical analysis module 390 may receive the three-dimensional position 360 of the ball determined by the ball positioning module 320 and / or the three-dimensional pose 370 of the player determined by the player pose detection module 330 to determine tactical data 395 .

[0078] In some embodiments, the three-dimensional rendering module 340 may be used to visually render the determined technical and tactical data 395 .

[0079] As an example, to implement the 3D rendering module 340, the EEVEE rendering engine in Blender 4.1 can be utilized, with Python programming controlling animation generation and interaction. Specifically, during data preprocessing and scene setup, a Python script can be written to parse the input sequence data of the ball's 3D position 360 and / or the player's 3D pose 370 and convert this data into a format compatible with Blender. Blender can also be used to create realistic court scenes, accurately setting up court, player, and ball models to ensure the animations match actual game scenarios. During animation generation and special effects creation, a Python script can be written to control the motion trajectories of the player and ball models based on the input sequence data, and collision detection and interaction logic can be added to simulate realistic game scenarios. In some embodiments, the 3D rendering module 340 can also design ball trajectory and / or shot effects. Blender's tracking and noise textures can be used to simulate trajectory dragging, and a particle system can be used to enhance the visual effect of the ball's flight. UV mapping can be used to apply a Y-axis transparency gradient to a 3D cone, thereby rendering shots as diffuse, rippling, and halo effects.

[0080] According to some embodiments of the present invention, the system 300 for presenting three-dimensional animation further provides users with realistic and highly interactive three-dimensional animation based on the ability to monitor and analyze technical and tactical skills, thereby better helping players and coaches analyze and improve technical and tactical skills.

[0081] Figure 4 Example animation frames presented by a system for presenting three-dimensional animation of tennis motion according to some embodiments of the present invention are shown.

[0082] Animation 410 shows real-time badminton match data analysis. The presented technical and tactical data includes the current number of shots (4), the speed of the last ball across the net for both players (50 km / h and 78 km / h), and the movement trajectories of both players.

[0083] Animation 450 shows a player's jump data analysis, including the player's jump times (27), average jump height (0.07 meters), and the location of each jump point.

[0084] Figure 5 An overall flow chart of a system for presenting three-dimensional animation of tennis motion according to some embodiments of the present invention is shown.

[0085] After the process starts, the video capture module 510 first captures motion videos from multiple perspectives in a time-synchronized manner to generate multiple single-perspective videos.

[0086] Each generated monoscopic video can be processed by a camera parameter estimation module, a ball positioning module, and a player pose detection module. Specifically, the camera parameter estimation module determines camera parameters based on each monoscopic video, the ball positioning module determines the two-dimensional trajectory of the ball based on each monoscopic video, and the player pose detection module determines the two-dimensional pose of the human target based on each monoscopic video.

[0087] The camera parameters, the 2D trajectory of the ball, and the 2D pose of the human target generated for each monoscopic video may then be combined to determine a 3D motion result 520. The 3D motion result may include the 3D trajectory of the ball and the 3D pose of the player.

[0088] The 3D motion result 520 can then be processed and analyzed by the technique and tactics analysis module 530 to obtain technique and tactics data. The 3D motion result 520 can also be rendered by the 3D rendering module 540 to output a rendered video.

[0089] According to another aspect of the present invention, a television broadcasting system for tennis sports is provided.

[0090] Figure 6 A block diagram of a television broadcast system 600 for tennis sports is shown according to some embodiments of the present invention.

[0091] The television broadcasting system 600 may include a server 610 , a data exchange system 620 , a frame synchronization card 630 , an online packaging system 640 , a super slow motion system 650 , a switcher 660 , and a television signal broadcasting system 670 .

[0092] The server 610 may include the system 300 for presenting three-dimensional animation described above in conjunction with 3 (and including the technical and tactical analysis module 390), which may provide the determined technical and tactical data (e.g., the technical and tactical analysis module 390 described above in conjunction with Figure 3 The described technical and tactical data 395) and the generated rendered video (for example, the above combination Figure 3 3D animation described in 380).

[0093] The technical and tactical data may be provided to the online packaging system 640 through the data exchange system. The online packaging system 640 may beautify and package the received technical and tactical data to obtain key signals.

[0094] The rendered video is transmitted to the super slow motion system 650 through the frame synchronization card 630. The super slow motion system 650 performs recording and editing on the received rendered video to generate a super slow motion picture.

[0095] The key signal generated by the online packaging system 640 and the super slow motion picture generated by the super slow motion system 650 are provided to the switcher 660. The switcher integrates the key signal and the super slow motion picture to generate a broadcast picture.

[0096] The broadcast images are transmitted to the television signal broadcast system 670. The television signal broadcast system 670 broadcasts television signals to television viewers based on the broadcast images.

[0097] The television broadcasting system 600 according to some embodiments of the present invention can broadcast the determined technical and tactical data and rendered video of the tennis sport to television viewers.

[0098] In some embodiments, television broadcast system 600 may include multiple time-synchronized cameras (not shown) for capturing multiple video frames of the motion.

[0099] Figure 7 A schematic diagram illustrating a camera arrangement 700 of video capture cameras for a television broadcast system for tennis sports according to some embodiments of the present invention is shown.

[0100] Figure 7 The camera arrangement 700 of the video acquisition cameras for the badminton court 710 is shown. The video acquisition cameras 721, 722, 723, 724, 725, and 726 can be arranged around the badminton court 710 to capture motion data in the badminton court 710. The video acquisition cameras 721, 722, 723, 724, 725, and 726 can be set as time-synchronized cameras, and their shooting frame rates are variable, and the default frame rate can be 50 frames per second. The video acquisition cameras 721, 722, 723, 724, 725, and 726 can be coupled to the server 730 via optical fibers, and the collected signals are transmitted to the server 730 via optical fibers. The server 730 can be a combination of the above Figure 6 Server 610 described above. Server 730 can run relevant software modules to store, process, and perform computational analysis on multiple video channels to obtain the 3D position of the ball and the 3D posture of the players. Server 730 can further provide the 3D position of the ball and the 3D posture of the players for further technical and tactical analysis, or for 3D visualization output via 3D rendering, or for broadcasting to television viewers via a television broadcast system.

[0101] Figure 7 The number and position of the video acquisition cameras shown in the figure are exemplary. In some embodiments, more or fewer video acquisition cameras can be used. In some embodiments, the position of each video acquisition camera can be adjusted.

[0102] According to another aspect of the present invention, a method for monitoring technical and tactical capabilities in tennis sports is provided.

[0103] Figure 8Flowchart showing a method 800 for monitoring technical and tactical capabilities of tennis sports according to some embodiments of the present invention. Figure 1 The technical and tactical capability monitoring system 100 is described as performing, but the scope of the present invention is not limited in this regard.

[0104] The method 800 may include, at block 810 , receiving a plurality of video frames of motion captured at a plurality of viewpoints by a plurality of time-synchronized cameras.

[0105] The method 800 may include, at block 820 , calculating camera parameters based on position information of elements associated with the pitch in the plurality of video frames using a neural network model.

[0106] The method 800 may include, at block 830 , determining a two-dimensional position of the ball from the plurality of video frames using a deep neural network model.

[0107] The method 800 may include, at block 840 , determining a three-dimensional position of the ball based on the determined two-dimensional position of the ball and the camera parameters.

[0108] Method 800 may include, at block 850 , determining two-dimensional poses of all detectable human targets from a plurality of video frames.

[0109] Method 800 may include, at block 860 , determining a three-dimensional pose of the human target based on the determined two-dimensional pose of the human target and camera parameters.

[0110] The method 800 may include, at block 870 , determining a three-dimensional pose of the player using the three-dimensional pose of the human target based on three-dimensional spatial geometric constraints associated with the court.

[0111] According to some embodiments of the present invention, the technical and tactical ability monitoring method 800 can effectively alleviate the occlusion problem by visually processing video frames collected from multiple perspectives during tennis sports, and the results are highly accurate. In addition, the method 800 can also provide the three-dimensional position of the ball and the three-dimensional posture of the player, thereby providing a solid foundation for further in-depth and extensive technical and tactical analysis.

[0112] Figure 9 A flowchart illustrating a process 900 for calculating camera parameters according to some embodiments of the present invention is shown. Process 900 may be a specific implementation of the steps in block 820 described above in conjunction with method 800, but the scope of the present invention is not limited in this regard.

[0113] Process 900 may include, at block 910 , detecting a plurality of key points of a pitch in a plurality of video frames.

[0114] Process 900 may include, at block 920 , matching a plurality of key points to a predefined standard three-dimensional court template.

[0115] Process 900 may include, at block 930, estimating camera parameters using an algorithm based on the matching results. In some embodiments, the step in block 930 may include estimating camera parameters based on a homography matching error between a homography transformation of the plurality of key points and a homography transformation of the standard 3D court template.

[0116] Figure 10 A flowchart illustrating a process 1000 for training a neural network model for calculating camera parameters according to some embodiments of the present invention is shown. Process 1000 may be used to train the above-mentioned neural network model. Figure 1 The camera parameter module 110 of the technical and tactical capability monitoring system 100 described herein or in combination Figure 8 The steps in block 820 of method 800 are described using a neural network, but the scope of the present invention is not limited in this regard.

[0117] Process 1000 may include, at block 1010 , arbitrarily manually annotating a threshold number of keypoints in each video frame, the threshold number being less than the number of the plurality of keypoints detected.

[0118] Process 1000 may include, at block 1020 , computing a homography transform of a court in a video frame to a standard three-dimensional court template for a threshold number of key points.

[0119] Process 1000 may include, at block 1030 , mapping intersection points of court lines associated with the court from a standard three-dimensional court template into a video frame based on a result of the homography transformation.

[0120] Process 1000 may include, at block 1040 , comparing the mapped intersection points to corresponding intersection points in a standard three-dimensional court template.

[0121] Process 1000 may include, at block 1050 , manually adjusting the comparison results to indicate locations in the video frame of one or more intersection points of maximum error.

[0122] Process 1000 may include, at block 1060 , adjusting the homography transformation using a method of minimizing remapping errors based on the adjusted positions of the one or more intersection points.

[0123] Figure 11 A flow chart illustrating a process 1100 for determining a two-dimensional position of a ball according to some embodiments of the present invention is shown. Process 1100 may be a specific implementation of the steps in block 830 described above in conjunction with method 800, but the scope of the present invention is not limited in this respect.

[0124] Process 1100 may include, at block 1100 , generating a heat map indicating areas where a ball is likely to appear and the probability density of the ball appearing in the areas.

[0125] Process 1100 may include, at block 1120 , determining the position of the ball from the heat map using a contour extraction method and / or a preferred maximum contour method.

[0126] Optionally, the process 1100 may further include adjusting the plurality of video frames to a fixed predetermined resolution at block 1130 , wherein the two-dimensional position of the ball is determined based on the video frames with the adjusted resolution.

[0127] Optionally, the process 1100 may further include: at block 1140 , receiving a plurality of consecutive video frames as input to the deep neural network.

[0128] Optionally, the process 1100 may further include: at block 1150 , performing a separate calculation on the background in the video frame at the same viewing angle, and superimposing the calculation result into the video frame.

[0129] Figure 12 A flow chart illustrating a process 1200 for determining a player's three-dimensional pose according to some embodiments of the present invention is shown. Process 1200 may be a specific implementation of the steps in block 870 described above in conjunction with method 800, but the scope of the present invention is not limited in this respect.

[0130] The process 1200 may include, at block 1210 , estimating a position range where the player will appear in a plurality of video frames at a next moment based on the player's three-dimensional pose and the camera parameters by using a back-projection technique.

[0131] Process 1200 may include, at block 1230 , skipping target detection within the position range and directly estimating the two-dimensional pose of the player at a next moment.

[0132] Process 1200 may include, at block 1250 , skipping the matching task and directly performing single-person triangulation on the player for the estimated next-time 2D pose of the player.

[0133] According to another aspect of the present invention, a computer-readable storage medium is provided.

[0134] Figure 13 A block diagram of a machine-readable storage medium 1300 is shown according to some embodiments of the present invention.

[0135] The computer readable storage medium 1300 may include computer instructions 1350 stored thereon. The computer instructions 1350, when processed, may implement the above-mentioned Figure 8 The method 800 described, combined with Figure 9 The process described in 900, combined Figure 10 The process described in 1000, combined Figure 11 The process described in 1100, combined Figure 12 Process 1200 is described.

[0136] According to another aspect of the present invention, a computer program product is provided.

[0137] Figure 14 A block diagram of a computer program product 1400 is shown according to some embodiments of the invention.

[0138] The computer program product 1400 may include computer instructions 1450. The computer instructions 1450, when processed, may implement the above-mentioned combination Figure 8 The method 800 described, combined with Figure 9 The process described in 900, combined Figure 10 The process described in 1000, combined Figure 11 The process described in 1100, combined Figure 12 Process 1200 is described.

[0139] The embodiments of the present invention have been described with reference to the accompanying drawings, which are intended to be illustrative rather than restrictive.

Claims

1. A system for monitoring technical and tactical capabilities of tennis players, comprising: Camera parameter estimation module, used for: receiving multiple video frames of motion captured by multiple time-synchronized cameras at multiple viewpoints; calculating camera parameters based on position information of features associated with the court in the plurality of video frames using a neural network model, The camera parameter estimation module is further configured to: detecting a plurality of key points of the court in the plurality of video frames; Matching the plurality of key points with a predefined standard three-dimensional court template; and Based on the matching results, an algorithm is used to estimate the camera parameters; Ball positioning module for: determining a two-dimensional position of the ball from the plurality of video frames using a deep neural network model, wherein the deep neural network model is trained to determine the two-dimensional position of the ball from consecutive video frames; determining a three-dimensional position of the ball based on the determined two-dimensional position of the ball and the camera parameters; and Player posture detection module, used for: determining two-dimensional poses of all detectable human objects from the plurality of video frames; Determining a three-dimensional pose of the human target based on the determined two-dimensional pose of the human target and the camera parameters; and Based on the three-dimensional spatial geometric relationship constraints associated with the court, the three-dimensional posture of the player is determined by the three-dimensional posture of the human target. The player posture detection module is also used to: Identify all detectable human targets through target detection algorithms; Generate a sequence of bounding boxes for each detected human target using a two-dimensional tracking algorithm; Cropping and enlarging the image in each bounding box in the bounding box sequence; and Determine the two-dimensional pose of the human target using a two-dimensional pose estimation algorithm; The player posture detection module is also used to: Based on the three-dimensional spatial geometric relationship constraints associated with the court, a predetermined number of players' three-dimensional postures are screened from all detectable three-dimensional postures of human targets, where the predetermined number is determined at least in part based on the number of players specified in the sport.

2. The system of claim 1, wherein: The camera parameter estimation module is used to: The camera parameters are estimated based on a homography matching error between the homography transformation of the plurality of key points and the homography transformation of the standard three-dimensional stadium template.

3. The system of claim 2, wherein: The neural network model is trained through the following steps: arbitrarily manually annotating a threshold number of key points in each video frame, wherein the threshold number is less than the number of the plurality of key points detected; Calculating a homography between the court in the video frame and the standard three-dimensional court template for the threshold number of key points; Based on the result of the homography transformation, mapping the intersection points of the court lines associated with the court from the standard three-dimensional court template to the video frame; comparing the mapped intersection points with corresponding intersection points in the standard three-dimensional court template; Manually fine-tuning the positions of one or more intersections indicating maximum errors as a result of the comparison in the video frame; The homography is adjusted based on the fine-tuned positions of the one or more intersection points by minimizing the remapping error.

4. The system of claim 1, wherein: The ball positioning module is further configured to perform at least one of the following steps: resizing the plurality of video frames to a fixed predetermined resolution, wherein a two-dimensional position of the ball is determined based on the resized video frames; Receiving a plurality of consecutive video frames as input of the deep neural network; The background in the video frame under the same viewing angle is calculated separately, and the calculation results are superimposed on the video frame.

5. The system of claim 1, wherein: The ball positioning module is used to: generating a heat map indicating areas where the ball is likely to appear and the probability density of the ball appearing in the areas; and The position of the ball is determined from the heat map using a contour extraction method and / or preferably a maximum contour method.

6. The system of claim 1, wherein: The player posture detection module is configured to perform the following steps for each tracked player: estimating the position range of the player in the multiple video frames at the next moment based on the player's three-dimensional posture and the camera parameters by back-projection technology; and Target detection is skipped within the position range, and the two-dimensional posture of the player at the next moment is directly estimated.

7. The system of claim 6, wherein: The player posture detection module is further configured to perform the following steps for each tracked player: For the estimated two-dimensional pose of the player at the next moment, the matching task is skipped and single-person triangulation of the player is directly performed.

8. A system for analyzing technical and tactical capabilities for tennis-related sports, comprising: The system according to any one of claims 1 to 7; as well as Technical and tactical analysis module for: Technical and tactical data are determined based on the determined three-dimensional position of the ball and / or the three-dimensional posture of the player, wherein the technical and tactical data includes at least one of the following: net crossing speed, net crossing height, net crossing angle, number of shots and point statistics, player movement distance, and player jump event.

9. A system for presenting a three-dimensional animation of a tennis-like sport, comprising: The system according to any one of claims 1 to 7; as well as 3D rendering module for: receiving data indicating a three-dimensional position of a ball and / or a three-dimensional pose of a player determined over a period of time; as well as The received data is rendered into a three-dimensional animation of the ball over a period of time, a three-dimensional animation of the players, or a three-dimensional animation reflecting the relationship between the players and the ball.

10. The system of claim 9, further comprising: Technical and tactical analysis module for: determining technical and tactical data based on the determined three-dimensional position of the ball and / or the three-dimensional posture of the players, and The three-dimensional rendering module is also used for: Visual rendering of the determined technical and tactical data.

11. A television broadcasting system for tennis sports, comprising: The system according to any one of claims 1 to 7; a technical and tactical analysis module, configured to determine technical and tactical data based on the determined three-dimensional position of the ball and / or the three-dimensional posture of the player, wherein the technical and tactical data includes at least one of the following: net speed, net height, net angle, number of shots and location statistics, player travel distance, and player jump events; 3D rendering module for: receiving data indicating a three-dimensional position of a ball and / or a three-dimensional pose of a player determined over a period of time; and Rendering the received data into a three-dimensional animation of the ball, a three-dimensional animation of the players, or a three-dimensional animation reflecting the relationship between the players and the ball over a period of time and / or visually rendering the technical and tactical data determined to obtain a rendered video; An online packaging module is used to beautify and package the technical and tactical data to obtain key signals; A super slow motion system, configured to record and edit the rendered video to generate a super slow motion image; a switcher for integrating and processing the key signal and the super slow motion picture to generate a broadcast picture; and A television signal broadcasting system is used to broadcast television signals to television viewers based on the broadcast images.

12. The television broadcasting system according to claim 11, further comprising: Multiple time-synchronized cameras are used to capture multiple video frames of motion from multiple viewpoints.

13. A method for monitoring technical and tactical abilities in tennis sports, comprising: receiving multiple video frames of motion captured by multiple time-synchronized cameras at multiple viewpoints; calculating camera parameters based on position information of features associated with the court in the plurality of video frames using a neural network model, The calculation of camera parameters includes: detecting a plurality of key points of the court in the plurality of video frames; Matching the plurality of key points with a predefined standard three-dimensional court template; and Based on the matching results, an algorithm is used to estimate the camera parameters; determining a two-dimensional position of the ball from the plurality of video frames using a deep neural network model, wherein the deep neural network model is trained to determine the two-dimensional position of the ball from consecutive video frames; determining a three-dimensional position of the ball based on the determined two-dimensional position of the ball and the camera parameters; determining the two-dimensional poses of all detectable human objects from the plurality of video frames, Among them, determining the two-dimensional posture of all detectable human targets includes: Identify all detectable human targets through target detection algorithms; Generate a sequence of bounding boxes for each detected human target using a two-dimensional tracking algorithm; Cropping and enlarging the image in each bounding box in the bounding box sequence; and Determine the two-dimensional pose of the human target using a two-dimensional pose estimation algorithm; Determining a three-dimensional pose of the human target based on the determined two-dimensional pose of the human target and the camera parameters; and Based on the three-dimensional spatial geometric relationship constraints associated with the court, the three-dimensional posture of the player is determined by the three-dimensional posture of the human target. Determining the player's three-dimensional posture includes: Based on the three-dimensional spatial geometric relationship constraints associated with the court, a predetermined number of players' three-dimensional postures are screened from all detectable three-dimensional postures of human targets, where the predetermined number is determined at least in part based on the number of players specified in the sport.

14. The method of claim 13, wherein: Estimating the camera parameters includes: estimating the camera parameters based on a homography matching error between the homography transformation of the plurality of key points and the homography transformation of the standard three-dimensional stadium template, and The neural network model is trained through the following steps: arbitrarily manually annotating a threshold number of key points in each video frame, wherein the threshold number is less than the number of the plurality of key points detected; Calculating a homography between the court in the video frame and the standard three-dimensional court template for the threshold number of key points; Based on the result of the homography transformation, mapping the intersection points of the court lines associated with the court from the standard three-dimensional court template to the video frame; comparing the mapped intersection points with corresponding intersection points in the standard three-dimensional court template; Manually fine-tuning the positions of one or more intersections indicating maximum errors as a result of the comparison in the video frame; The homography is adjusted based on the fine-tuned positions of the one or more intersection points by minimizing the remapping error.

15. The method of claim 13, wherein: Determining the two-dimensional position of the ball includes at least one of the following steps: resizing the plurality of video frames to a fixed predetermined resolution, wherein a two-dimensional position of the ball is determined based on the resized video frames; Receiving a plurality of consecutive video frames as input of the deep neural network; The background in the video frame under the same viewing angle is calculated separately, and the calculation results are superimposed on the video frame.

16. The method of claim 13, wherein: Determining the 2D position of the ball involves: generating a heat map indicating areas where the ball is likely to appear and the probability density of the ball appearing in the areas; and The position of the ball is determined from the heat map using a contour extraction method and / or preferably a maximum contour method.

17. The method of claim 13, wherein: Determining the three-dimensional posture of a player through the three-dimensional posture of a human target includes: estimating a position range of the player in the multiple video frames at a next moment based on the player's three-dimensional posture and the camera parameters by back-projection technology; skipping target detection within the position range and directly estimating the two-dimensional pose of the player at the next moment; and For the estimated two-dimensional pose of the player at the next moment, the matching task is skipped and single-person triangulation of the player is directly performed.

18. A computer-readable storage medium having computer instructions stored thereon, wherein when the computer instructions are executed by a processor, the steps of the method according to any one of claims 13 to 17 are implemented.

19. A computer program product comprising computer instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 13 to 17.

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