Player sports data monitoring and analyzing method and system combined with image analysis

Abstract

The invention provides a player motion data monitoring and analyzing method and system combined with image analysis, and belongs to the technical field of equipment management, and the method specifically comprises the steps: carrying out the monitoring of the motion data of players according to the distribution data of different players in the gathering time periods of different monitoring deviation regions in combination with the switching delay condition of the regional view angle of a panoramic binocular camera in the gathering time periods, and carrying out the monitoring of the motion data of the players; determining a coordinate fusion object of the UWB tag and the monitoring image in the player, performing real-time fusion processing of the coordinates of the UWB tag and the monitoring image on the coordinate fusion object, and monitoring the monitoring deviation area according to the coordinate fusion object data in the aggregation time period of the monitoring deviation area in the movement process. And determining the coordinate fusion processing result of the UWB tag of the player without the coordinate fusion object and the monitoring image, thereby improving the reliability of monitoring processing of the player.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of image recognition, and particularly relates to a player movement data monitoring and analyzing method and system combined with image analysis. BACKGROUND

[0002] With the development of technology, more and more companies develop devices to assist in the training of players. In the prior art, uwb technology is often used for assisted training. Specifically, base stations are arranged around the court, and players calculate the coordinate information of the players on the court by wearing a bracelet carrying a uwb positioning chip, and transmit the coordinate data to the background server. The background server processes the received coordinate data, obtains the real-time position of the player, and analyzes the movement through coordinate changes, and finally obtains the running distance, running speed, running frequency, running hot zone and formation position of the player.

[0003] In addition, the prior art also uses images to analyze and detect players. Specifically, a similar technical solution is given in the invention patent application CN201610797573.0 "a ball image recognition method and system". However, the above technical solutions all have the following technical problems: Single use of images or uwb positioning chips has problems. On the one hand, single use of images can lead to a too complex analysis process of player movement data. On the other hand, single use of uwb positioning chips has a certain positioning accuracy. The general uwb positioning error is about 10 cm. When multiple people gather together, it may not be possible to distinguish the specific person. Therefore, by combining image and uwb positioning chip for monitoring and analyzing player movement data, the reliability of monitoring and processing player movement data can be greatly improved. However, in order to realize the fusion processing of coordinates in the image coordinate system and the uwb coordinate system, especially when there are many players on the court, the fusion processing of coordinates is difficult, so it becomes a technical problem to be solved how to determine the fusion processing mode of different players in the area where the monitoring of the monitoring image has a deviation and the distribution of the player gathering period.

[0004] To solve the above technical problems, the application provides a player movement data monitoring and analyzing method and system combined with image analysis. SUMMARY

[0005] To achieve the purpose of the application, the application adopts the following technical solutions: Specifically, the application provides a player movement data monitoring and analyzing method combined with image analysis, which specifically includes: S1 determines player images in each court area according to the analysis result of the monitoring image of the panoramic view angle of the panoramic binocular camera in the court, and determines the monitoring deviation area in the court area based on the player images; S2 determines the aggregation period of the monitoring deviation area in the starting period of the movement process based on the distribution aggregation of the players, and determines the coordinate fusion object of the UWB tag in the players and the monitoring image when the switching matching of the regional view angle of the panoramic binocular camera between the aggregation periods meets the requirements, according to the distribution data of the players in the aggregation period of the monitoring deviation area, and the switching delay of the regional view angle of the panoramic binocular camera. S3 performs real-time fusion processing of the coordinates of the UWB tag and the monitoring image on the coordinate fusion object, and determines the fusion processing result of the UWB tag and the monitoring image of the player excluding the coordinate fusion object according to the coordinate fusion object data in the aggregation period of the monitoring deviation area in the movement process.

[0006] The beneficial effects of the present application are: The distribution data of the players in the aggregation period of each monitoring deviation area, and the switching delay of the regional view angle of the panoramic binocular camera in the aggregation period are used to determine the coordinate fusion object of the UWB tag in the players and the monitoring image, which realizes the identification of the players with a large number of aggregation periods and a large number of monitoring deviation areas, and further switches the switching delay of the corresponding monitoring deviation area of the aggregation period, realizes the identification of the players with a large aggregation probability and a low identification processing reliability, and further realizes the real-time fusion processing of the UWB tag and the monitoring image, thereby ensuring the reliability and accuracy of the monitoring processing of the movement data of the players.

[0007] According to the coordinate fusion object data in the aggregation period of the monitoring deviation area in the movement process, the fusion processing result of the UWB tag and the monitoring image of the player excluding the coordinate fusion object is determined, which realizes the determination of the dynamic coordinate fusion processing result of the UWB tag and the monitoring image of the player excluding the coordinate fusion object in the movement process, avoids the technical problem that the number of coordinate fusion objects is small, and the monitoring processing reliability of the movement data of the player excluding the coordinate fusion object is poor, and through the dynamic determination of the coordinate fusion processing result, the technical problem of too high data processing difficulty caused by real-time fusion processing of all players is also avoided, and balanced control processing of data processing difficulty and monitoring reliability of movement data is realized.

[0008] Further, the panoramic view angle of the panoramic binocular camera is the view angle for monitoring the entire court, which is specifically determined according to the camera for monitoring the entire court.

[0009] Further, the player image is determined according to the analysis result of the player in the monitoring image in different court areas.

[0010] Further, the method for determining the monitoring deviation area in the court area is: determining the size of the player in the monitoring image in different court areas according to the analysis result of the monitoring deviation area in the court area; determining whether the court area is a monitoring deviation area based on the size.

[0011] Further, the method for determining the coordinate fusion processing result of the UWB tag of the player and the monitoring image is: determining the number of coordinate fusion objects in the aggregation period of different monitoring deviation areas in the movement process according to the composition of the coordinate fusion objects in the aggregation period of different monitoring deviation areas in the movement process; determining the fusion matching period in the aggregation period of the monitoring deviation area according to the number of coordinate fusion objects and the number of players in the aggregation period of the monitoring deviation area; determining the coordinate fusion processing result of the UWB tag of the player and the monitoring image by the number of fusion matching periods of different monitoring deviation areas and in combination with the distribution data of the player in different aggregation periods.

[0012] Specifically, the movement process is a period from the start to the current time in the current game.

[0013] In a second aspect, the application provides a player movement data monitoring and analysis system combined with image analysis, which adopts the above-mentioned player movement data monitoring and analysis method combined with image analysis, and specifically comprises: a UWB tag, a base station, a panoramic binocular camera, and a data processing module. The UWB tag is arranged on the player, the base station installed in the court is responsible for acquiring the positioning signal of the UWB tag, the panoramic binocular camera includes two visual angles and is responsible for acquiring the monitoring image of the court, and the data processing module is responsible for coordinate fusion processing of the UWB tag of the player and the monitoring image.

[0014] Other features and advantages will be set forth in the following description, and the objects and other advantages of the application will be achieved and obtained by the structure specifically pointed out in the description and the drawings.

[0015] In order to make the above-mentioned objects, features and advantages of the application more obvious and easy to understand, the following preferred embodiments are specifically described, and the accompanying drawings are referred to for detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0016] The above and other features and advantages of the present application will become more apparent by describing in detail its example embodiments with reference to the attached drawings.

[0017] Figure 1 is a flowchart of a method for determining a coordinate fusion object of a player motion data monitoring and analysis method combined with image analysis; Figure 2 is a flowchart of a method for determining a monitoring deviation region in a field area; Figure 3 is a flowchart of a method for determining that the switching matching of the regional view angle of the panoramic binocular camera between different aggregation time periods meets the requirements; Figure 4 is a flowchart of a method for determining a UWB tag in a player and a coordinate fusion object of a monitoring image; Figure 5 is a schematic diagram of a monitoring image of a panoramic view angle of a panoramic binocular camera in the present application; Figure 6 is a schematic diagram of a monitoring image of a regional view angle of a panoramic binocular camera in the present application; Figure 7 is a schematic diagram of a monitoring position of a player in a sports process. DETAILED DESCRIPTION

[0018] In order for those skilled in the art to better understand the technical solutions in the present specification, the technical solutions in the present specification will be described clearly and completely below in conjunction with the drawings in the present specification. Obviously, the described embodiments are only some of the embodiments of the present specification, not all. Based on the embodiments of the present specification, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present specification.

[0019] In the present application, firstly, according to the distribution of the aggregation time period of the players in the monitoring deviation region of the panoramic binocular camera in the first few minutes of the sports, the determination of the coordinate fusion object of the real-time fusion processing of the UWB tag and the coordinates of the monitoring image is realized in real time, and according to the composition of the coordinate fusion object of the aggregation time period in the sports process, the fusion processing of the UWB tag and the coordinates of the monitoring image is performed on the players whose identification of the coordinates is difficult, thereby improving the efficiency of data analysis and processing.

[0020] Embodiment 1 As shown in Figure 1 The present application provides a player motion data monitoring and analysis method combined with image analysis, specifically comprising: S1 determines player images in each of the court regions based on the analysis result of the monitoring image in the panoramic view of the panoramic binocular camera in the court, and determines a monitoring deviation region in the court region based on the player images; When the size of the player in the monitoring image is less than 0.1 times the size of the monitoring image, the court region is determined as a monitoring deviation region.

[0021] Further, the panoramic view of the panoramic binocular camera is a view for monitoring the entire court, and is specifically determined according to a camera for monitoring the entire court.

[0022] Further, the player images are determined according to the analysis result of the player in the monitoring image in different court regions.

[0023] It can be understood that the court regions are divided according to a preset unit area, or the court is equally divided into court regions according to a preset number, and in a possible embodiment, the preset number is in a range of 10 to 20.

[0024] Specifically, as shown in Figure 2 The method for determining the monitoring deviation region in the court region is: The size of the player in the monitoring image in different court regions is determined based on the analysis result of the monitoring deviation region in the court region; Whether the court region is a monitoring deviation region is determined based on the size.

[0025] It can be understood that when the size of the player in the monitoring image does not meet the requirement, the court region is determined as a monitoring deviation region, and in a possible embodiment, when the size of the player in the monitoring image is less than 0.1 times the size of the monitoring image, the court region is determined as a monitoring deviation region, wherein the threshold value is determined according to the size of the monitoring image, and the larger the size of the monitoring image, the smaller the threshold value.

[0026] Optionally, the method for determining the monitoring deviation region in the court region is: The size of the court region in the monitoring image is determined based on the analysis result of the monitoring deviation region in the court region; Whether the court region is a monitoring deviation region is determined based on the size of the court region in the monitoring image.

[0027] It should be noted that when the size of the court region in the monitoring image is less than a preset size threshold, the court region is determined as a monitoring deviation region.

[0028] 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. 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.

[0029] 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.

[0030] 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.

[0031] 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: 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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: 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.

[0037] 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. 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.

[0038] 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: 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; 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; 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.

[0039] 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.

[0040] 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.

[0041] 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: 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; 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. 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.

[0042] 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.

[0043] 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.

[0044] Furthermore, real-time fusion processing of the coordinates of UWB tags and monitoring images is performed, specifically including: 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; 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.

[0045] 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.

[0046] 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: 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 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.

[0047] Specifically, the "movement process" refers to the time period from the start of the match to the current moment.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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: 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. 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.

[0053] 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.

[0054] The matching deviation region is determined based on the distribution of the fusion matching time period in the aggregation time period; 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.

[0055] 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.

[0056] 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. 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.

[0057] 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.

[0058] Example 2 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: 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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 for monitoring and analyzing player movement data combining image analysis, characterized in that, 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. Based on the coordinate fusion object data in the clustering period of the monitoring deviation area during the movement, the coordinate fusion processing result of the player's UWB tags and monitoring image is determined after removing the coordinate fusion object.

2. The player movement data monitoring and analysis method combining image analysis as described in claim 1, characterized in that, 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 combining image analysis as described in claim 1, characterized in that, 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, 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; Based on the dimensions, determine whether the court area is a monitoring deviation area.

5. The player movement data monitoring and analysis method combining image analysis as described in claim 4, characterized in that, If 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.

6. The player movement data monitoring and analysis method combining image analysis as described in claim 1, characterized in that, The clustering period refers to the time period in which multiple players are present in the monitored deviation area.

7. 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.

8. The player movement data monitoring and analysis method combining image analysis as described in claim 7, 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.

9. 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 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; 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.

10. 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 1-9, 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.

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