A method and system for monitoring and analyzing player movement data by combining image analysis

Abstract

This invention provides a method and system for monitoring and analyzing player movement data by combining image analysis, belonging to the field of equipment management technology. Specifically, it includes: using the distribution data of different players in different monitoring deviation areas during the aggregation period, and combining the switching delay of the regional perspective of the panoramic binocular camera during the aggregation period, determining the coordinate fusion object of the UWB tag of the player and the monitoring image; performing real-time fusion processing of the coordinate fusion object of the UWB tag and the monitoring image coordinates; and determining the coordinate fusion processing result of the player's UWB tag and the monitoring image after removing the coordinate fusion object based on the coordinate fusion object data of the aggregation period of the monitoring deviation area during the movement, thereby improving the reliability of player monitoring and processing.

Description

Technical Field

[0001] This invention belongs to the field of image recognition technology, and in particular relates to a method and system for monitoring and analyzing player movement data by combining image analysis. Background Technology

[0002] With the development of technology, more and more companies are developing equipment to assist players in training. Existing technical solutions often use UWB technology for training assistance. Specifically, base stations are set up around the field, and athletes wear wristbands with UWB positioning chips to calculate the player's coordinate information on the field and transmit the coordinate data to the backend server. The backend server processes the received coordinate data, obtains the athlete's real-time position, and performs motion analysis through coordinate changes. Finally, it obtains the player's running distance, running speed, number of runs, running hot zone, and formation position.

[0003] In addition, existing technical solutions also utilize images for player analysis and detection. A similar technical solution is presented in invention patent application CN201610797573.0, "A Ball Image Recognition Method and System." However, all of the above technical solutions suffer from the following technical problems:

[0004] Relying solely on images or UWB positioning chips presents challenges. Firstly, relying solely on images leads to overly complex player movement data analysis. Secondly, using only UWB positioning chips is problematic because these chips often have limited positioning accuracy (typically around 10cm), making it difficult to distinguish individuals when multiple players are clustered together. Therefore, combining images and UWB positioning chips for player movement data monitoring and analysis can significantly improve the reliability of this process. However, achieving coordinate fusion between the image and UWB coordinate systems, especially when dealing with a large number of players on the field, presents significant challenges. This necessitates addressing the critical technical issue of determining the appropriate fusion method for different players by considering the distribution of player clusters in areas with monitoring errors.

[0005] To address the aforementioned technical problems, this application provides a method and system for monitoring and analyzing player movement data that combines image analysis. Summary of the Invention

[0006] To achieve the objectives of this invention, the following technical solution is adopted:

[0007] Specifically, this application provides a method for monitoring and analyzing player movement data by combining image analysis, which includes:

[0008] S1 uses the analysis results of the panoramic view monitoring images from the panoramic binocular camera in the stadium to determine the player images in each stadium area, and determines the monitoring deviation area in the stadium area based on the player images.

[0009] Based on the distribution and aggregation of players, S2 determines the aggregation period of the monitoring deviation area during the initial period of the movement process. According to the distribution of the aggregation period, when the switching matching of the regional view of the panoramic binocular camera between the aggregation periods meets the requirements, the distribution data of players in the aggregation period of the monitoring deviation area and the switching delay of the regional view of the panoramic binocular camera are used to determine the coordinate fusion object of the UWB tag in the player and the monitoring image.

[0010] S3 performs real-time fusion processing of the coordinates of the UWB tag and the coordinates of the monitoring image on the coordinate fusion object, and determines the coordinate fusion processing result of the player's UWB tag and the monitoring image after removing the coordinate fusion object based on the coordinate fusion object data in the clustering period of the monitoring deviation area during the movement.

[0011] The beneficial effects of this invention are as follows:

[0012] By analyzing the distribution data of players during the clustering periods in various monitoring deviation areas and the switching delay of the panoramic binocular camera's regional perspective during the clustering periods, the coordinate fusion object of the UWB tag in the player and the monitoring image is determined. This enables the identification of players with a large number of clustering periods and a large number of monitoring deviation areas. Furthermore, by analyzing the switching delay of the monitoring deviation area corresponding to the clustering period, the identification of players with a high probability of clustering and low reliability of identification processing is achieved. Finally, by fusing the coordinates of the UWB tag and the monitoring image in real time, the reliability and accuracy of the player's motion data monitoring and processing are ensured.

[0013] Based on the coordinate fusion object data during the aggregation period of the monitoring deviation area during the movement, the coordinate fusion processing result of the player's UWB tag and the monitoring image after removing the coordinate fusion object is determined. This realizes the dynamic determination of the coordinate fusion processing result of the player's UWB tag and the monitoring image after removing the coordinate fusion object during the movement. This avoids the technical problem of poor monitoring and processing reliability of the movement data of the player after removing the coordinate fusion object due to the small number of coordinate fusion objects. By dynamically determining the coordinate fusion processing result, it also reduces the technical problem of excessive data processing difficulty caused by real-time fusion processing of all players. It achieves a balanced control of data processing difficulty and monitoring reliability of movement data.

[0014] Furthermore, the panoramic view of the panoramic binocular camera is the view used to monitor the entire stadium, and the specific view is determined based on the camera used to monitor the entire stadium.

[0015] Furthermore, the player images are determined based on the analysis results of players in surveillance images in different court areas.

[0016] Furthermore, the method for determining the monitoring deviation area in the court area is as follows:

[0017] Based on the analysis results of the monitoring deviation areas in the court area, the size of the players in different court areas in the monitoring image is determined;

[0018] Based on the dimensions, determine whether the court area is a monitoring deviation area.

[0019] Furthermore, the method for determining the coordinate fusion result of the player's UWB tag removed from the coordinate fusion object and the monitoring image is as follows:

[0020] The number of coordinate fusion objects in the aggregation period of different monitoring deviation areas during the movement is determined by the composition of the coordinate fusion objects in the aggregation period of different monitoring deviation areas during the movement.

[0021] Based on the number of coordinate fusion objects and the number of players in the clustering period of the monitoring deviation area, the fusion matching period in the clustering period of the monitoring deviation area is determined;

[0022] By fusion matching the number of time periods in different monitoring deviation areas and combining the player's distribution data in different aggregation time periods, the fusion processing result of the player's UWB tag and the coordinates of the monitoring image is determined.

[0023] Specifically, the "movement process" refers to the time period from the start of the match to the current moment.

[0024] Secondly, the present invention provides a player motion data monitoring and analysis system combining image analysis, employing the aforementioned player motion data monitoring and analysis method combining image analysis, specifically including:

[0025] UWB tags, base stations, panoramic binocular cameras, and data processing modules;

[0026] The UWB tags are attached to the players, and the base stations installed in the stadium are responsible for acquiring the positioning signals of the UWB tags. The panoramic binocular camera has two perspectives and is responsible for acquiring monitoring images of the stadium. The data processing module is responsible for fusion processing of the coordinates of the players' UWB tags and the monitoring images.

[0027] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0028] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0029] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0030] Figure 1 This is a flowchart of a player motion data monitoring and analysis method that combines image analysis;

[0031] Figure 2 This is a flowchart illustrating the method for determining the monitoring deviation area within the court area;

[0032] Figure 3 This is a flowchart illustrating a method for determining whether the switching and matching of the regional viewpoints of a panoramic binocular camera meets the requirements during different aggregation periods.

[0033] Figure 4 This is a flowchart illustrating the method for determining the object by fusing the UWB tags of players with the coordinates of surveillance images;

[0034] Figure 5 This is a schematic diagram of a panoramic view monitoring image from the panoramic binocular camera in this application.

[0035] Figure 6 This is a schematic diagram of a monitoring image from the regional perspective of the panoramic binocular camera in this application;

[0036] Figure 7 This is a diagram illustrating the monitoring positions of players during a game. Detailed Implementation

[0037] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0038] In this application, the distribution of multiple players clustering in the monitoring deviation area of ​​the panoramic binocular camera during the first few minutes of the movement is first determined based on the distribution of players clustering in the monitoring deviation area. Then, the coordinate fusion object is determined for real-time fusion processing of UWB tags and monitoring image coordinates. Based on the composition of the coordinate fusion object during the clustering period of the movement, the coordinate fusion processing of UWB tags and monitoring image coordinates is performed on players whose coordinates are difficult to identify, thereby improving the efficiency of data analysis and processing.

[0039] Example 1

[0040] like Figure 1 As shown, this application provides a method for monitoring and analyzing player movement data by combining image analysis, specifically including:

[0041] S1 uses the analysis results of the panoramic view monitoring images from the panoramic binocular camera in the stadium to determine the player images in each stadium area, and determines the monitoring deviation area in the stadium area based on the player images.

[0042] When the ratio of the player's size in the monitoring image to the size of the monitoring image is less than 0.1, the court area is identified as a monitoring deviation area.

[0043] Furthermore, the panoramic view of the panoramic binocular camera is the view used to monitor the entire stadium, and the specific view is determined based on the camera used to monitor the entire stadium.

[0044] Furthermore, the player images are determined based on the analysis results of players in surveillance images in different court areas.

[0045] It is understood that the court area is divided according to a preset unit area, or the court can be divided into equal areas according to a preset number. In one possible embodiment, the preset number is between 10 and 20.

[0046] Specifically, such as Figure 2 As shown, the method for determining the monitoring deviation area in the court area is as follows:

[0047] Based on the analysis results of the monitoring deviation areas in the court area, the size of the players in different court areas in the monitoring image is determined;

[0048] Based on the dimensions, determine whether the court area is a monitoring deviation area.

[0049] It is understood that when the size of the player in the monitoring image does not meet the requirements, the court area is determined to be a monitoring deviation area. In one possible embodiment, when the ratio of the player's size in the monitoring image to the size of the monitoring image is less than 0.1, the court area is determined to be a monitoring deviation area. The threshold is determined based on the size of the monitoring image; the larger the size of the monitoring image, the smaller the threshold.

[0050] Optionally, the method for determining the monitoring deviation area in the court area is as follows:

[0051] Based on the analysis results of the monitoring deviation area in the described court area, the size of the court area in the monitoring image is determined;

[0052] Whether the court area is a monitoring deviation area is determined based on the size of the court area in the monitoring image.

[0053] It should be noted that when the size of the court area in the monitoring image is smaller than a preset size threshold, the court area is determined to be a monitoring deviation area.

[0054] Based on the distribution and aggregation of players, S2 determines the aggregation period of the monitoring deviation area during the initial period of the movement process. According to the distribution of the aggregation period, when the switching matching of the regional view of the panoramic binocular camera between the aggregation periods meets the requirements, the distribution data of players in the aggregation period of the monitoring deviation area and the switching delay of the regional view of the panoramic binocular camera are used to determine the coordinate fusion object of the UWB tag in the player and the monitoring image.

[0055] Specifically, when the number of clustering periods with switching processing delays is less than 3, it is determined that the switching matching of the panoramic binocular camera's regional viewpoint between different clustering periods meets the requirements.

[0056] Furthermore, the clustering period is the period in which multiple players exist in the monitoring deviation area. In one possible embodiment, the period in which the number of players in the monitoring deviation area is greater than three is defined as the clustering period.

[0057] Specifically, the area view is the view used to monitor a portion of the court area. By continuously switching angles, different areas of the court are monitored. The specific camera used for monitoring a portion of the court area is determined based on the panoramic binocular camera.

[0058] Specifically, such as Figure 3 As shown, the requirements for matching the switching of the panoramic binocular camera's regional field of view between different aggregation periods are met, specifically including:

[0059] Based on the distribution of different aggregation periods, determine the switching processing time for switching the regional perspective corresponding to the next aggregation period to the monitoring deviation area;

[0060] The switching processing delay is determined based on the overlap between the switching processing duration and the monitoring deviation area corresponding to the next aggregation period.

[0061] Based on the switching processing delay of different aggregation periods, determine whether the switching matching of the regional viewpoint of the panoramic binocular camera meets the requirements between different aggregation periods.

[0062] In one possible embodiment, when switching from aggregation period A to the adjacent aggregation period B, if there are no multiple players in the monitoring deviation area corresponding to aggregation period A, that is, after aggregation period A ends, and then switching to aggregation period B, since there is a certain overlap between aggregation period A and aggregation period B, the duration corresponding to the overlap period is used as the switching processing delay, which is also part of the switching processing duration.

[0063] It should be noted that when the number of clustered periods with switching processing delays meets the requirements, that is, when the number of clustered periods with switching processing delays is less than the preset threshold, the switching matching of the panoramic binocular camera's regional viewpoint between different clustered periods is determined to meet the requirements.

[0064] It should be noted that, in order to ensure the reliability of player monitoring and processing, during the initial period of the movement, the coordinate fusion object is generally determined only within the first preset duration of the movement, or in one possible embodiment, within the first 5 minutes. That is, the clustering period within the preset duration is determined. Therefore, based on this, the preset quantity threshold is determined according to the preset duration. The longer the duration, the larger the preset quantity threshold. In one possible embodiment, when the preset duration is 5 minutes, if the number of clustering periods with switching processing delay is less than 3, it is determined that the switching matching of the panoramic binocular camera's regional viewpoint between different clustering periods meets the requirements. When the ratio of the switching processing delay to the duration of the clustering period is greater than 0.2, it is determined that there is a switching processing delay in the clustering period.

[0065] It should be noted that when the switching and matching of the panoramic binocular camera's regional viewpoint between different aggregation periods does not meet the requirements, the coordinates of all players' UWB tags and the monitoring images are fused in real time.

[0066] Optionally, it is necessary to determine whether the switching and matching of the panoramic binocular camera's regional field of view meets the requirements during different aggregation periods, specifically including:

[0067] Based on the distribution of different aggregation time periods, the number of monitoring deviation areas belonging to the aggregation time period in different aggregation time periods is determined, and the discrete aggregation time periods are classified according to whether the monitoring deviation areas are in the same monitoring image in the regional view of the panoramic binocular camera.

[0068] Optionally, if the number of gathering time periods is too large in the above steps, that is, if the number of gathering time periods within the preset duration is greater than the preset threshold for the number of gathering time periods, then it is determined that the switching and matching of the regional view of the panoramic binocular camera between different gathering time periods does not meet the requirements.

[0069] The distributed discrete clustering period refers to the monitoring deviation area belonging to the clustering period that cannot appear in the same monitoring image in the regional view of the panoramic binocular camera. In other words, no matter how the regional view of the panoramic binocular camera is switched, it is impossible for it to appear in the same monitoring image.

[0070] Specifically, such as Figure 4 As shown, the method for determining the object of fusion between the UWB tag in the player and the coordinates of the monitoring image is as follows:

[0071] Based on the distribution data of the players during the clustering periods in different monitoring deviation areas, determine the number of clustering periods in which the players exist in different monitoring deviation areas;

[0072] Based on the number of time periods during which the players cluster in the monitoring deviation area, a matching monitoring area is determined within the monitoring deviation area;

[0073] Based on the switching delay of the panoramic binocular camera's regional perspective in different monitoring areas during different aggregation periods, it is determined whether the player is a coordinate fusion object.

[0074] It is understood that the matching monitoring area is a monitoring deviation area where the number of clustered time periods meets the requirements. In one possible embodiment, the monitoring deviation area where the ratio of the number of clustered time periods of the player in the monitoring deviation area to the number of clustered time periods in the monitoring deviation area is greater than 0.6 is used as the matching monitoring area of ​​the player.

[0075] It should be noted that when the player has a matching monitoring area where the switching delay does not meet the requirements, the player is determined to be a coordinate fusion object. In one possible embodiment, if the number of clustered periods of switching processing delay accounts for more than 0.3% of the clustered periods in the monitoring deviation area, the player is determined to have a switching delay that does not meet the requirements.

[0076] Optionally, the method for determining the object of fusion between the UWB tag in the player and the coordinates of the monitoring image is as follows:

[0077] Based on the distribution data of the players during the clustering periods in different monitoring deviation areas, determine the number of clustering periods in which the players exist in different monitoring deviation areas;

[0078] Based on the switching delay of the panoramic binocular camera's regional viewpoint in different monitoring deviation areas during different aggregation periods, the number of aggregation periods with switching processing delays is determined.

[0079] Based on the number of time periods in which the player is clustered in different monitoring deviation areas and the number of time periods in which there is a switching processing delay, it is determined whether the player is a coordinate fusion object.

[0080] In one possible embodiment, the proportion of the number of clustered periods with different monitoring deviation areas is used as the basic weight value. The deviation distribution matching value is determined by the sum of the products of the number of clustered periods of the player in different monitoring deviation areas and the basic weight value, which is the weight of the number of clustered periods in different monitoring deviation areas. When the deviation distribution matching value is greater than the preset matching threshold, the player is determined to be the coordinate fusion object.

[0081] S3 performs real-time fusion processing of the coordinates of the UWB tag and the coordinates of the monitoring image on the coordinate fusion object, and determines the coordinate fusion processing result of the player's UWB tag and the monitoring image after removing the coordinate fusion object based on the coordinate fusion object data in the clustering period of the monitoring deviation area during the movement.

[0082] Furthermore, real-time fusion processing of the coordinates of UWB tags and monitoring images is performed, specifically including:

[0083] Based on the multiple markers placed at known locations within the court, the UWB device can directly obtain the three-dimensional coordinates of these markers in the UWB coordinate system;

[0084] The panoramic binocular camera calculates the three-dimensional coordinates of these calibration objects in the camera coordinate system using binocular vision algorithms;

[0085] By identifying the correspondence between the three-dimensional coordinates of multiple calibration objects in the UWB coordinate system and the three-dimensional coordinates in the camera coordinate system, the rotation matrix R and translation vector T are calculated. The player position coordinates obtained from the UWB tags are then transformed into the camera coordinate system using the rotation matrix R and translation vector T, enabling real-time fusion processing of the coordinates of the UWB tags and the monitoring images.

[0086] It should be noted that when determining the result of the coordinate fusion processing of the UWB tags of the players removed from the coordinate fusion object and the coordinates of the monitoring image, the identification process is performed according to a preset time period. In one possible embodiment, the preset time period can be any value between 3 and 10 minutes. That is, the result of the coordinate fusion processing of the UWB tags of the players removed from the coordinate fusion object and the coordinates of the monitoring image is determined according to the preset time period. The specific preset time period is determined according to the number of players removed from the coordinate fusion object, and the more players removed from the coordinate fusion object, the shorter the preset time period.

[0087] Furthermore, the method for determining the coordinate fusion result of the player's UWB tag removed from the coordinate fusion object and the monitoring image is as follows:

[0088] The number of coordinate fusion objects in the aggregation period of different monitoring deviation areas during the movement is determined by the composition of the coordinate fusion objects in the aggregation period of different monitoring deviation areas during the movement.

[0089] Based on the number of coordinate fusion objects and the number of players in the clustering period of the monitoring deviation area, the fusion matching period in the clustering period of the monitoring deviation area is determined;

[0090] By fusion matching the number of time periods in different monitoring deviation areas and combining the player's distribution data in different aggregation time periods, the fusion processing result of the player's UWB tag and the coordinates of the monitoring image is determined.

[0091] Specifically, the "movement process" refers to the time period from the start of the match to the current moment.

[0092] Furthermore, the fusion matching period is a clustering period in which the ratio of the number of coordinate fusion objects to the number of players in the clustering period of the monitoring deviation area meets the requirements. In one possible embodiment, a clustering period in which the ratio of the number of coordinate fusion objects to the number of players in the clustering period of the monitoring deviation area is greater than 0.8 is used as the fusion matching period.

[0093] It should be noted that if the number of fusion matching time periods for different monitoring deviation areas meets the requirements, that is, the proportion of fusion matching time periods for different monitoring deviation areas in the monitoring deviation time periods is greater than 0.9, the monitoring reliability of different monitoring deviation areas is relatively high. Since there are a large number of players to be fused, after excluding the players to be fused, the positions of other players can be determined only by UWB tags. In this case, it is not necessary to perform coordinate fusion processing between the UWB tags of the players removed from the coordinate fusion object and the coordinates of the monitoring image.

[0094] Furthermore, it is understood that if there are monitoring deviation areas where the number of fusion matching time periods does not meet the requirements, the number of monitoring deviation areas where the number of fusion matching time periods does not meet the requirements is determined. If the number of monitoring deviation areas where the number of fusion matching time periods does not meet the requirements is greater than the preset threshold for the number of deviation areas, in one possible embodiment, if the proportion of the number of monitoring deviation areas where the number of fusion matching time periods does not meet the requirements to the total number of monitoring deviation areas is greater than 0.25, then when the coordinate fusion processing of the UWB tags of the players removed from the coordinate fusion object and the coordinates of the monitoring image is performed, real-time fusion processing of the coordinates of the UWB tags and the monitoring image is performed.

[0095] If the number of monitoring deviation areas that do not meet the requirements for the number of fusion matching time periods is not greater than the preset threshold for the number of deviation areas, then the monitoring deviation areas that do not meet the requirements for the number of fusion matching time periods are taken as matching deviation areas. When the player whose coordinate fusion object is removed appears in the clustering time period of the matching deviation area, that is, when he is one of the players in the clustering time period, the UWB tag of the player whose coordinate fusion object is removed is fused with the coordinate of the monitoring image. Real-time fusion processing of the UWB tag and the coordinate of the monitoring image is performed. Other players can be located by simply using the UWB tag. In this case, it is not necessary to perform the fusion processing of the UWB tag of the player whose coordinate fusion object is removed with the coordinate of the monitoring image.

[0096] Furthermore, the method for determining the coordinate fusion result of the player's UWB tag removed from the coordinate fusion object and the monitoring image is as follows:

[0097] The player whose coordinate fusion object is removed is taken as the target player. The number of coordinate fusion objects in the aggregation period of different monitoring deviation areas during the movement is determined based on the composition of coordinate fusion objects in the aggregation period of different monitoring deviation areas during the movement. The fusion matching period in the aggregation period of the monitoring deviation area is determined based on the number of coordinate fusion objects and the number of players in the aggregation period of the monitoring deviation area.

[0098] Optionally, if the number of fusion matching time periods for different monitoring deviation areas meets the requirements in the above steps, that is, the proportion of fusion matching time periods for different monitoring deviation areas in the monitoring deviation time periods is greater than 0.9, then the monitoring reliability of different monitoring deviation areas is not high. Since there are a large number of players to be fused, after excluding the players to be fused, the positions of other players can be determined only by UWB tags. In this case, it is not necessary to perform coordinate fusion processing between the UWB tags of the players to be fused and the coordinates of the monitoring images.

[0099] In addition, it should be noted that if the number of fusion matching time periods in different monitoring deviation areas does not meet the requirements, it is necessary to determine whether the ratio of the number of fusion matching time periods to the number of clustered time periods is greater than the preset proportion threshold. If so, the positions of other players can be determined simply by using UWB tags. In this case, it is not necessary to perform coordinate fusion processing of the UWB tags of the players removed from the coordinate fusion objects and the coordinates of the monitoring images. In other cases, proceed to the next step.

[0100] The matching deviation region is determined based on the distribution of the fusion matching time period in the aggregation time period;

[0101] It should be noted that if the number of monitoring deviation areas that do not meet the requirements of the number of fusion matching time periods is greater than the preset threshold for the number of deviation areas, in one possible embodiment, if the proportion of the number of monitoring deviation areas that do not meet the requirements of the number of fusion matching time periods to the total number of monitoring deviation areas is greater than 0.25, then when the coordinate fusion processing of the UWB tags of the players removed from the coordinate fusion object and the coordinates of the monitoring images is performed, real-time fusion processing of the coordinates of the UWB tags and the monitoring images will be carried out.

[0102] The number of clustered periods in which the target player appears is determined and used as the matching clustered periods. Based on the number of matching clustered periods and the overlap data with the clustered periods in the matching deviation area, the coordinate fusion processing result of the target player is determined.

[0103] It is understandable that the clustering period in which the target player appears is one of the players clustered in the clustering period. In the above steps, if the number of matching clustering periods is large, that is, greater than the preset number threshold, then the target player is determined to undergo real-time fusion processing of UWB tags and the coordinates of the monitoring image.

[0104] Furthermore, when the number of matching aggregation time periods is not greater than a preset number threshold, it is necessary to further determine whether the number of monitoring deviation areas corresponding to the matching aggregation time periods meets the requirements. When the number of monitoring deviation areas corresponding to the matching aggregation time periods is greater than the preset deviation area number threshold, it is determined that the target player will undergo real-time fusion processing of the coordinates of the UWB tag and the monitoring image.

[0105] Additionally, it can be understood that if the number of monitoring deviation areas corresponding to the matching aggregation period is greater than the preset deviation area number threshold, and if the proportion of the target player appearing in the matching aggregation period in the matching deviation area among all matching aggregation periods is greater than the preset proportion threshold, that is, when the UWB tag of the player excluding the coordinate fusion object is fused with the coordinate of the monitoring image, real-time fusion processing of the UWB tag and the coordinate of the monitoring image is performed. Other players can be located simply by using their UWB tags. In this case, it is not necessary to perform the fusion processing of the UWB tag of the player excluding the coordinate fusion object with the coordinate of the monitoring image.

[0106] Example 2

[0107] Secondly, the present invention provides a player motion data monitoring and analysis system combining image analysis, employing the aforementioned player motion data monitoring and analysis method combining image analysis, specifically including:

[0108] UWB tags, base stations, panoramic binocular cameras, and data processing modules;

[0109] The UWB tags are attached to the players, and the base stations installed in the stadium are responsible for acquiring the positioning signals of the UWB tags. The panoramic binocular camera has two perspectives and is responsible for acquiring monitoring images of the stadium. The data processing module is responsible for fusion processing of the coordinates of the players' UWB tags and the monitoring images.

[0110] UWB (Ultra-Wideband) technology is a technology that uses extremely short pulse signals to achieve high-precision positioning, ranging, and wireless communication. It features strong anti-interference capabilities, high positioning accuracy, and low power consumption.

[0111] It can deploy four base stations at the four corners of the field, and coordinate ranging based on these four base stations. By sending and receiving ultra-wideband electromagnetic waves (frequency band typically between 300MHz and 10GHz) in extremely short pulses (nanosecond level) through the base stations, the target position is calculated using information such as the time of flight (ToF), time difference of arrival (TDoA), or phase difference of the signal.

[0112] By using UWB receiving devices such as wristbands worn by each player, he can pinpoint the location of each player, specifically, such as... Figure 5 The image shown is a schematic diagram of the panoramic view monitoring image of the panoramic binocular camera in this application. Figure 6 This is a schematic diagram of a monitoring image from the regional perspective of the panoramic binocular camera in this application.

[0113] To combine the advantages of panoramic stereo cameras and UWB technology, and to more accurately locate player positions and obtain real-time game data, we first need to calibrate the panoramic stereo cameras and UWB devices, aligning the camera coordinate system with the UWB device coordinate system.

[0114] The core of calibration is establishing the transformation relationship between the panoramic stereo camera coordinate system and the UWB device coordinate system. We obtain the coordinate information of the same set of calibration objects in both coordinate systems, and use mathematical transformations to find the rotation matrix R and translation vector T from the UWB coordinate system to the camera coordinate system, thereby achieving coordinate transformation.

[0115] In practice, multiple markers at known locations are placed within the court. UWB devices can directly acquire the 3D coordinates (xuwb, yuwb, zuwb) of these markers in the UWB coordinate system. The panoramic binocular camera then calculates the 3D coordinates (xcam, ycam, zcam) of these markers in the camera coordinate system using a binocular vision algorithm. By using multiple sets of such corresponding coordinate data, the rotation matrix R and translation vector T are solved. Subsequently, the player position coordinates acquired by the UWB devices can be transformed into the camera coordinate system and fused with the image information captured by the camera for analysis. Figure 7 The diagram shown illustrates the monitoring positions of players during movement.

[0116] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0117] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0118] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method of monitoring and analyzing player movement data in combination with image analysis, characterized by, Specifically, it includes: Based on the analysis results of the panoramic monitoring images from the panoramic binocular cameras in the stadium, player images in each stadium area are determined, and monitoring deviation areas in the stadium area are determined based on the player images. Based on the distribution and aggregation of players, the aggregation period of the monitoring deviation area in the initial period of the movement process is determined. According to the distribution of the aggregation period, when the switching matching of the regional view of the panoramic binocular camera between the aggregation periods meets the requirements, the distribution data of players in the aggregation period of the monitoring deviation area and the switching delay of the regional view of the panoramic binocular camera are used to determine the coordinate fusion object of the UWB tag in the player and the monitoring image. The coordinate fusion object is subjected to real-time fusion processing of UWB tags and monitoring image coordinates, and based on the coordinate fusion object data in the clustering period of the monitoring deviation area during the movement, the coordinate fusion processing results of UWB tags and monitoring images of other players excluding the coordinate fusion object are determined. The method for determining the monitoring deviation area in the court area is as follows: Based on the analysis results of the monitoring deviation areas in the court area, the size of the players in different court areas in the monitoring image is determined; If the size of the player in the monitoring image does not meet the requirements, then the court area is determined to be a monitoring deviation area; The clustering period refers to the time period during which multiple players are present in the monitored deviation area; The method for determining the coordinate fusion result of the UWB tags of players other than the coordinate fusion object and the monitoring image is as follows: The number of coordinate fusion objects in the aggregation period of different monitoring deviation areas during the movement is determined by the composition of the coordinate fusion objects in the aggregation period of different monitoring deviation areas during the movement. Based on the number of coordinate fusion objects and the number of players in the clustering period of the monitoring deviation area, the fusion matching period in the clustering period of the monitoring deviation area is determined; By matching the number of time periods in different monitoring deviation areas and combining the player's distribution data in different aggregation time periods, the fusion processing result of the player's UWB tag and the coordinates of the monitoring image is determined. The fusion matching period is the period in which the ratio of the number of coordinate fusion objects to the number of players in the clustering period of the monitoring deviation area meets the requirements. If the number of fusion matching periods for different monitoring deviation areas meets the requirements, then after excluding the players in the coordinate fusion process, it is not necessary to perform coordinate fusion processing of the UWB tags of other players (excluding the coordinate fusion objects) with the monitoring images. If there are monitoring deviation areas where the number of fusion matching periods does not meet the requirements, and if the number of monitoring deviation areas where the number of fusion matching periods does not meet the requirements is greater than the preset deviation area number threshold, then when performing coordinate fusion processing of the UWB tags of other players (excluding the coordinate fusion objects) with the monitoring images, real-time fusion processing of the coordinates of the UWB tags and the monitoring images is performed.

2. The method of player movement data monitoring analysis in conjunction with image analysis of claim 1, wherein, The panoramic view of the binocular camera is the perspective from which the entire stadium is monitored.

3. The player movement data monitoring and analysis method in connection with image analysis as claimed in claim 1, characterized by, The player images are determined based on the analysis results of players in surveillance images from different court areas.

4. The player movement data monitoring and analysis method combining image analysis as described in claim 1, characterized in that, Determine if the switching and matching of the panoramic binocular camera's regional field of view between different aggregation periods meets the requirements, specifically including: Based on the distribution of different aggregation periods, determine the switching processing time for switching the regional perspective corresponding to the next aggregation period to the monitoring deviation area; The switching processing delay is determined based on the overlap between the switching processing duration and the monitoring deviation area corresponding to the next aggregation period. Based on the switching processing delay of different aggregation periods, determine whether the switching matching of the regional viewpoint of the panoramic binocular camera meets the requirements between different aggregation periods.

5. The player movement data monitoring and analysis method combining image analysis as described in claim 4, characterized in that, When the switching and matching of the panoramic binocular camera's regional viewpoint between different aggregation periods does not meet the requirements, the coordinates of all players' UWB tags and the monitoring images are fused in real time.

6. The player movement data monitoring and analysis method combining image analysis as described in claim 1, characterized in that, Real-time fusion processing of UWB tag and surveillance image coordinates is performed, specifically including: Based on the multiple known locations of the markers placed within the court, the UWB device directly obtains the three-dimensional coordinates of these markers in the UWB coordinate system; The panoramic binocular camera calculates the three-dimensional coordinates of these calibration objects in the camera coordinate system using binocular vision algorithms; By identifying the correspondence between the three-dimensional coordinates of multiple calibration objects in the UWB coordinate system and the three-dimensional coordinates in the camera coordinate system, the rotation matrix R and translation vector T are calculated. The player position coordinates obtained from the UWB tags are then transformed into the camera coordinate system using the rotation matrix R and translation vector T, enabling real-time fusion processing of the coordinates of the UWB tags and the monitoring images.

7. A player motion data monitoring and analysis system combining image analysis, employing the player motion data monitoring and analysis method combining image analysis as described in any one of claims 1-6, characterized in that, Specifically, it includes: UWB tags, base stations, panoramic binocular cameras, and data processing modules; The UWB tags are attached to the players, and the base stations installed in the stadium are responsible for acquiring the positioning signals of the UWB tags. The panoramic binocular camera has two perspectives and is responsible for acquiring monitoring images of the stadium. The data processing module is responsible for fusion processing of the coordinates of the players' UWB tags and the monitoring images.

Images