Intelligent monitoring method and system for billiard match data

Abstract

The invention discloses a billiard match data intelligent monitoring method and system, and relates to the technical field of data processing, and the method comprises the following steps: obtaining a monitoring video of a target billiard match area; decomposing the monitoring video frame by frame to obtain a plurality of monitoring images; cue posture information of a target player and actual track information of a target billiard ball are obtained; according to the posture information of the billiard cue, theoretical track information is obtained from a historical competition database of the target player; whether the actual track information is matched with the theoretical track information or not is judged, if yes, the monitoring video of the target billiard competition area is obtained, and if not, a plurality of abnormal factors are obtained based on the monitoring image; inputting the plurality of abnormal factors into a preset evaluation model to obtain a cheating evaluation value; whether the cheating evaluation value is larger than a preset standard value or not is judged, if yes, cheating confirmation information is output, and the billiard cheating behavior monitoring method and device have the advantage that the billiard cheating behavior monitoring accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for intelligently monitoring billiards game data. Background Art

[0002] Traditional billiards games generally rely on manual referees to judge the scores and violations of billiard players, which are easily affected by subjective factors and difficult to capture hidden cheating behaviors in real time, such as micro-movements of the hands or cheating with equipment. The existing machine vision recognition methods used to assist in monitoring billiard game data can only be analyzed based on the trajectory of the billiard ball. Affected by the player's movement habits, technical level and other personal conditions, there is a great deal of randomness in relying solely on the analysis of the trajectory of the billiard ball. It is difficult to accurately identify whether a billiard player is cheating, and the monitoring results have large errors. Summary of the invention

[0003] The main purpose of the present application is to provide a method and system for intelligently monitoring billiard game data, aiming to solve the technical problem that the existing billiard game data monitoring method has low accuracy in monitoring cheating behavior of players.

[0004] To achieve the above purpose, the present application provides a method for intelligently monitoring billiards game data, comprising the following steps: Obtain monitoring video of the target billiards game area; Decompose the monitoring video frame by frame to obtain multiple monitoring images; According to the multiple monitoring images, the target player's billiard cue posture information and the actual trajectory information of the target billiard ball are obtained; wherein the billiard cue posture information includes the motion amplitude, displacement speed and hitting angle of the billiard cue, and the target billiard ball includes the cue ball and the hit ball; According to the billiard cue posture information, theoretical trajectory information is obtained from a historical match database of a target player; wherein the historical match database stores analysis data of the target player's billiard matches of the same type; Determine whether the actual trajectory information matches the theoretical trajectory information. If so, return to obtaining the monitoring video of the target billiards competition area. If not, obtain multiple abnormal factors based on the monitoring image; wherein the abnormal factors are state parameters of the target player when cheating; Inputting multiple abnormal factors into a preset evaluation model to obtain a cheating evaluation value; Determine whether the cheating evaluation value is greater than a preset standard value. If so, output cheating confirmation information. If not, return to obtaining the monitoring video of the target billiards competition area.

[0005] Optionally, multiple abnormal factors are obtained based on the monitoring image, including: Identify the target player's image information in the monitoring image to obtain the target player's behavior abnormality R; wherein the behavior abnormality R represents the abnormality of the target player's behavior and micro-expression; According to the monitoring image, the trajectory difference G between the actual trajectory information and the theoretical trajectory information is obtained; wherein the trajectory difference G represents the difference between the actual trajectory travel of the cue ball and the hit ball and the theoretical trajectory travel; According to the monitoring image, the angle deviation E of the hit ball is obtained; wherein the angle deviation E represents the angle deviation between the actual trajectory of the initial segment of the hit ball and the theoretical trajectory, and the actual trajectory of the initial segment is the actual movement trajectory of the hit ball from the time of being hit to the next collision time or the time of stopping.

[0006] Optionally, the expression for evaluating the model is: Q = (n + 1) (K1 R+K2 G+K3 E); In the formula, Q is the cheating evaluation value, n is the target player's historical cheating times, K1 is the first conversion coefficient, K2 is the second conversion coefficient, and K3 is the third conversion coefficient.

[0007] Optionally, the theoretical trajectory information includes a theoretical trajectory stroke of the cue ball and a theoretical trajectory stroke of the hit ball, and both the theoretical trajectory stroke of the cue ball and the theoretical trajectory stroke of the hit ball are range values; According to the monitoring image, the trajectory difference G between the actual trajectory information and the theoretical trajectory information is obtained, including: According to the monitoring image, the actual trajectory of the cue ball and the actual trajectory of the hit ball are obtained; Compare the actual trajectory of the cue ball with the theoretical trajectory of the cue ball to obtain a first trajectory difference G1; Compare the actual trajectory of the hit ball with the theoretical trajectory of the hit ball to obtain a second trajectory difference G2; According to the first trajectory difference G1 and the second trajectory difference G2, the trajectory difference G is obtained; wherein G=W1 G1+W2 G2, W1 is the first weight value, W2 is the second weight value, and W1<W2.

[0008] Optionally, comparing the actual trajectory of the cue ball with the theoretical trajectory of the cue ball to obtain a first trajectory difference G1 includes: Determine whether the actual trajectory of the cue ball is within the range of the theoretical trajectory of the cue ball; If yes, then the output first trajectory difference G1 is 0; If not, then obtain a first difference between the maximum or minimum value of the actual trajectory of the cue ball and the theoretical trajectory of the cue ball, and output the first difference as a first trajectory difference G1.

[0009] Optionally, the actual trajectory of the hit ball is compared with the theoretical trajectory of the hit ball to obtain a second trajectory difference G2, including: Determine whether the actual trajectory of the ball being hit is within the range of the theoretical trajectory of the ball being hit; If yes, then the output second trajectory difference G2 is 0; If not, then obtain a second difference between the maximum or minimum value of the actual trajectory of the hit ball and the theoretical trajectory of the hit ball, and output the second difference as a second trajectory difference G2.

[0010] Optionally, obtaining the angle deviation E of the hit ball according to the monitoring image includes: According to the monitoring image, the batting state parameters of the target player are obtained to obtain the initial segment theoretical trajectory of the batted ball; wherein the initial segment theoretical trajectory is the theoretical moving trajectory of the batted ball from the moment of being hit to the next collision moment or the stopping moment, and the initial segment theoretical trajectory has two trajectory boundaries; According to the monitoring image, the actual trajectory of the initial segment of the ball being hit is obtained; Determine whether the actual trajectory of the initial segment is between the boundaries of the two trajectories; If so, the output angle deviation E is 0; If not, the angle between the actual trajectory of the initial segment and the adjacent trajectory boundary is obtained, and the angle is output as the angle deviation E.

[0011] Optionally, identifying the image information of the target player in the monitoring image to obtain the behavior abnormality R of the target player includes: Identify the image information of the target player in the monitoring image; Compare the target player's image information with a preset image database to obtain the abnormal behavior index R1 and the abnormal micro-expression index R2; wherein the image database stores the image information of billiards game behavior norms; According to the abnormal behavior index R1 and the abnormal micro-expression index R2, the abnormal behavior degree R is obtained; wherein R=R1+R2.

[0012] Optionally, according to the billiard cue posture information, theoretical trajectory information is obtained from a historical game database of the target player, including: Get the current billiards game type of the target player to obtain the historical game database of the same type; Inputting the billiard cue posture information into the historical game database and performing similarity association to determine whether associated trajectory information matching the input billiard cue posture information can be found; If so, the matched associated trajectory information is output as theoretical trajectory information; If not, the cue stick posture information is input into a preset trajectory prediction model to obtain theoretical trajectory information.

[0013] To achieve the above purpose, the present application also provides a billiards game data intelligent monitoring system, including: A video acquisition module is used to acquire monitoring video of a target billiards game area; An image decomposition module is used to decompose the monitoring video frame by frame to obtain multiple monitoring images; An image recognition module is used to obtain the target player's billiard cue posture information and the actual trajectory information of the target billiard ball according to multiple monitoring images; wherein the billiard cue posture information includes the billiard cue's movement amplitude, displacement speed and hitting angle, and the target billiard ball includes the cue ball and the hit ball; An information matching module is used to obtain theoretical trajectory information from a historical match database of a target player according to the billiard cue posture information; wherein the historical match database stores analysis data of the target player's billiard matches of the same type; The first data processing module is used to determine whether the actual trajectory information matches the theoretical trajectory information. If so, it returns to obtain the monitoring video of the target billiards competition area. If not, it obtains multiple abnormal factors based on the monitoring image; wherein the abnormal factors are environmental state parameters when the target player cheats; A cheating assessment module, used to input multiple abnormal factors into a preset assessment model to obtain a cheating assessment value; The second data processing module is used to determine whether the cheating evaluation value is greater than a preset standard value. If so, it outputs cheating confirmation information; if not, it returns to obtaining the monitoring video of the target billiards competition area.

[0014] To achieve the above objectives, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.

[0015] To achieve the above objectives, the present application also provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the above method.

[0016] The beneficial effects that this application can achieve are as follows: The present application is based on the monitoring video of the target billiards game area, which can be decomposed into multiple monitoring images. The image recognition technology can be used to obtain the billiard cue posture information of the target player and the actual trajectory information of the target billiard ball. Combined with the billiard cue movement amplitude, displacement speed and hitting angle in the billiard cue posture information, the theoretical trajectory information matching the billiard cue posture information can be obtained from the historical game database of the target player, and then it is determined whether the actual trajectory information matches the theoretical trajectory information. If so, the monitoring video is obtained to continue monitoring. If not, it indicates that there is an abnormal situation suspected of cheating. In order to improve the monitoring accuracy, multiple abnormal factors are further obtained based on the monitoring image, and the multiple abnormal factors are input into a preset evaluation model, so that a cheating evaluation value can be obtained, and then it is determined whether the cheating evaluation value is greater than a preset standard value. If so, it indicates that cheating exists. At this time, cheating confirmation information can be output to prompt the referee. If not, the monitoring video is continued to be obtained for monitoring. To sum up, the present application is based on matching and comparing the target player's game status data with his historical game database, which is highly targeted and has more accurate data matching. It can reduce the data comparison error caused by factors such as different players' personal billiards technical levels and habits. At the same time, it integrates multiple abnormal factors to quantitatively calculate a cheating assessment value, which can realize the quantitative assessment of the probability of cheating, thereby improving the accuracy of monitoring the player's cheating behavior, which is conducive to assisting the referee in making fair and just decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the prior art description. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0018] Figure 1 A schematic diagram of a flow chart of a method for intelligently monitoring billiards game data in an embodiment of the present application; Figure 2 A schematic diagram of the actual trajectory of the target billiard ball in the embodiment of the present application; Figure 3 It is a schematic diagram of the case where the actual trajectory of the initial segment deviates from the theoretical trajectory of the initial segment in an embodiment of the present application.

[0019] Reference numerals: 110-actual trajectory of the cue ball, 120-actual trajectory of the hit ball, 130-trajectory boundary, 140-actual trajectory of the initial segment.

[0020] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0023] In this application, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0024] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by this application. Embodiment 1

[0025] Reference Figure 1-Figure 3 This embodiment provides a method for intelligently monitoring billiards game data, comprising the following steps: Obtain monitoring video of the target billiards game area; Decompose the monitoring video frame by frame to obtain multiple monitoring images; According to the multiple monitoring images, the target player's billiard cue posture information and the actual trajectory information of the target billiard ball are obtained; wherein the billiard cue posture information includes the motion amplitude, displacement speed and hitting angle of the billiard cue, and the target billiard ball includes the cue ball and the hit ball; According to the billiard cue posture information, theoretical trajectory information is obtained from a historical match database of a target player; wherein the historical match database stores analysis data of the target player's billiard matches of the same type; Determine whether the actual trajectory information matches the theoretical trajectory information. If so, return to obtaining the monitoring video of the target billiards competition area. If not, obtain multiple abnormal factors based on the monitoring image; wherein the abnormal factors are state parameters of the target player when cheating; Inputting multiple abnormal factors into a preset evaluation model to obtain a cheating evaluation value; Determine whether the cheating evaluation value is greater than a preset standard value. If so, output cheating confirmation information. If not, return to obtaining the monitoring video of the target billiards competition area.

[0026] In this embodiment, the monitoring video based on the target billiards game area can be decomposed into multiple monitoring images. The billiard cue posture information of the target player and the actual trajectory information of the target billiard ball can be obtained by using image recognition technology. Combined with the motion amplitude, displacement speed and hitting angle of the billiard cue in the billiard cue posture information, the theoretical trajectory information matching the billiard cue posture information can be obtained from the historical game database of the target player, and then it is determined whether the actual trajectory information matches the theoretical trajectory information. If so, the monitoring video is obtained to continue monitoring. If not, it indicates that there is an abnormal situation suspected of cheating. In order to improve the monitoring accuracy, multiple abnormal factors are further obtained based on the monitoring image, and the multiple abnormal factors are input into a preset evaluation model, so that a cheating evaluation value can be obtained, and then it is determined whether the cheating evaluation value is greater than a preset standard value. If so, it indicates that cheating exists. At this time, cheating confirmation information can be output to prompt the referee. If not, the monitoring video is continued to be obtained for monitoring. In summary, this embodiment is based on the matching and comparison between the target player's game status data and his historical game database, which is highly targeted and has more accurate data matching. It can reduce the data comparison error caused by factors such as the personal billiards technical level and habits of different players. At the same time, it integrates multiple abnormal factors to quantitatively calculate a cheating assessment value, which can realize the quantitative assessment of the cheating probability, thereby improving the accuracy of monitoring the player's cheating behavior, which is conducive to assisting the referee to make fair and just decisions.

[0027] It should be noted that the monitoring image also contains comprehensive characteristic image information such as billiard balls, billiard tables, billiard cues and target players, which is convenient for separately enlarging and identifying the image information required for image recognition, so as to carry out targeted image processing and recognition; the billiard balls, billiard tables, billiard cues and target players can also be separately monitored by video positioning tracking, so as to obtain corresponding image information respectively, and the recognition is more accurate.

[0028] As an optional implementation, multiple abnormal factors are obtained based on the monitoring image, including: Identify the target player's image information in the monitoring image to obtain the target player's behavior abnormality R; wherein the behavior abnormality R represents the abnormality of the target player's behavior and micro-expression; According to the monitoring image, the trajectory difference G between the actual trajectory information and the theoretical trajectory information is obtained; wherein the trajectory difference G represents the difference between the actual trajectory travel of the cue ball and the hit ball and the theoretical trajectory travel; According to the monitoring image, the angle deviation E of the hit ball is obtained; wherein the angle deviation E represents the angle deviation between the actual trajectory 140 of the initial segment of the hit ball and the theoretical trajectory, and the actual trajectory 140 of the initial segment is the actual movement trajectory of the hit ball from the time of being hit to the next collision time or the stop time.

[0029] In this embodiment, the target player's behavior and micro-expression changes can be identified simultaneously based on the monitoring image. If the player has cheating behavior (such as interfering with the opponent's actions), there will be obvious abnormal behavior characteristics. At the same time, considering that the cheating mentality is easily reflected through micro-expressions, the abnormal micro-expression characteristics can be combined for comprehensive consideration to obtain the behavior abnormality degree R; because under normal batting actions, the impact energy and loss of the billiard ball can be predicted based on the batting angle, the amplitude of the billiard cue movement, and the number of billiard ball impacts, etc., the theoretical trajectory travel of the mother ball and the hit ball can be obtained respectively. The theoretical trajectory travel is the total length of the trajectory route that the mother ball or the hit ball moves from the starting point to the landing point, and then the actual trajectory travel is compared with the theoretical trajectory travel. If there is cheating with the equipment (for example, a modified billiard cue that does not comply with the regulations is used), the actual trajectory travel will obviously exceed or be lower than the theoretical trajectory travel. Therefore, the difference between the actual trajectory travel of the cue ball and the hit ball and the theoretical trajectory travel is calculated to obtain the trajectory difference G. At the same time, it is also considered that the player may use small hand movements to affect the running trajectory of the hit ball when hitting the ball. At this time, the initial segment actual trajectory 140 of the hit ball can be intuitively reflected. Therefore, the angle deviation between the initial segment actual trajectory 140 and the theoretical trajectory is output as the angle deviation E. This embodiment comprehensively considers the three abnormal factors of behavior abnormality R, trajectory difference G and angle deviation E, and the evaluation of the probability of cheating is more comprehensive and sufficient, and the reference data is accurate and effective.

[0030] As an optional implementation, the expression of the evaluation model is: Q = (n + 1) (K1 R+K2 G+K3 E); In the formula, Q is the cheating evaluation value, n is the target player's historical cheating times, K1 is the first conversion coefficient, K2 is the second conversion coefficient, and K3 is the third conversion coefficient.

[0031] In this embodiment, the sum of the behavior abnormality R, the trajectory difference G and the angle deviation E is used to characterize the cheating evaluation value Q, but the properties of the three parameters are different, because each parameter is converted and adjusted by the first conversion coefficient K1, the second conversion coefficient K2 and the third conversion coefficient K3 respectively, so that each parameter can be quantitatively superimposed, and the first conversion coefficient K1, the second conversion coefficient K2 and the third conversion coefficient K3 can also include a weight coefficient, so as to characterize the influence of different parameter factors on the cheating evaluation value Q. At the same time, the historical cheating times n of the target player are also introduced. The historical cheating times n can be obtained based on the historical game database of the target player. If a cheating record is detected, the cheating evaluation value Q will increase exponentially to represent that the player has a greater cheating probability under the premise of having a cheating record. Therefore, based on the above formula, not only the parameters of each abnormal factor are taken into account, but also the historical cheating times of the player are taken into account to comprehensively evaluate the cheating probability of the player. Its quantitative calculation is effective and accurate, and has good reference and guidance.

[0032] As an optional implementation, the theoretical trajectory information includes the theoretical trajectory of the cue ball and the theoretical trajectory of the hit ball, and both the theoretical trajectory of the cue ball and the theoretical trajectory of the hit ball are range values; According to the monitoring image, the trajectory difference G between the actual trajectory information and the theoretical trajectory information is obtained, including: According to the monitoring image, the actual trajectory of the cue ball 110 and the actual trajectory of the hit ball 120 are obtained; Compare the actual trajectory 110 of the cue ball with the theoretical trajectory of the cue ball to obtain a first trajectory difference G1; Compare the actual trajectory 120 of the hit ball with the theoretical trajectory of the hit ball to obtain a second trajectory difference G2; According to the first trajectory difference G1 and the second trajectory difference G2, the trajectory difference G is obtained; wherein G=W1 G1+W2 G2, W1 is the first weight value, W2 is the second weight value, and W1<W2.

[0033] In this embodiment, when a player uses equipment to cheat, there is a probability that the actual trajectory of the cue ball and the hit ball does not match the theoretical trajectory. The kinetic energy of the hit can be comprehensively characterized according to the hitting angle, movement amplitude, hitting speed, etc., so that the kinetic energy consumption of the cue ball and the hit ball after the collision and subsequent collision can be predicted, so as to predict the range values ​​of the theoretical trajectory of the cue ball and the theoretical trajectory of the hit ball, respectively. Then, according to the monitoring image, the actual trajectory of the cue ball 110 and the actual trajectory of the hit ball 120 can be accurately calculated, and the difference is compared with the maximum or minimum value of the theoretical trajectory of the cue ball and the theoretical trajectory of the hit ball, respectively, to obtain the corresponding first trajectory difference G1 and second trajectory difference G2. At the same time, considering that in the case of cheating, the motion trajectory of the hit ball changes more obviously than that of the cue ball, the second trajectory difference G2 is also given a larger weight value W2, so that the final calculated trajectory difference G is more real and reliable, providing an accurate data basis.

[0034] As an optional implementation, the actual trajectory 110 of the cue ball is compared with the theoretical trajectory of the cue ball to obtain a first trajectory difference G1, including: Determine whether the actual trajectory of the cue ball 110 is within the range of the theoretical trajectory of the cue ball; If yes, then the output first trajectory difference G1 is 0; If not, then obtain a first difference between the maximum or minimum value of the actual trajectory of the cue ball 110 and the theoretical trajectory of the cue ball, and output the first difference as a first trajectory difference G1.

[0035] In the present embodiment, since the theoretical trajectory of the cue ball is a range value, it is necessary to first determine whether the actual trajectory 110 of the cue ball is within the range of the theoretical trajectory of the cue ball. If so, it means that the total stroke of the cue ball's motion trajectory is normal. At this time, the first trajectory difference G1 is output as 0. If it is not within the range, at this time, if the actual trajectory 110 of the cue ball is greater than the theoretical trajectory of the cue ball, then the difference between the actual trajectory 110 of the cue ball and the maximum value of the theoretical trajectory of the cue ball is the first trajectory difference G1. If the actual trajectory 110 of the cue ball is less than the theoretical trajectory of the cue ball, then the difference between the minimum value of the theoretical trajectory of the cue ball and the actual trajectory 110 of the cue ball is the first trajectory difference G1. The calculation is accurate and reliable.

[0036] As an optional implementation, the actual trajectory 120 of the hit ball is compared with the theoretical trajectory of the hit ball to obtain a second trajectory difference G2, including: Determine whether the actual trajectory of the ball being hit 120 is within the range of the theoretical trajectory of the ball being hit; If yes, then the output second trajectory difference G2 is 0; If not, then a second difference between the actual trajectory 120 of the hit ball and the maximum or minimum value of the theoretical trajectory of the hit ball is obtained, and the second difference is output as a second trajectory difference G2.

[0037] In the present embodiment, similarly, when calculating the second trajectory difference G2, it is first determined whether the actual trajectory stroke 120 of the hit ball is within the range of the theoretical trajectory stroke of the hit ball. If so, the second trajectory difference G2 is output as 0. If not, it is necessary to determine whether the actual trajectory stroke 120 of the hit ball is greater than the maximum value or less than the minimum value of the theoretical trajectory stroke of the hit ball, so as to calculate the corresponding second difference, and the second trajectory difference G2 can be accurately and effectively calculated.

[0038] As an optional implementation, obtaining the angle deviation E of the hit ball according to the monitoring image includes: According to the monitoring image, the batting state parameters of the target player are obtained to obtain the initial segment theoretical trajectory of the batted ball; wherein the initial segment theoretical trajectory is the theoretical moving trajectory of the batted ball from the moment of being hit to the next collision moment or the stopping moment, and the initial segment theoretical trajectory has two trajectory boundaries 130; According to the monitoring image, the actual trajectory of the initial segment of the hit ball is obtained 140; Determine whether the initial segment actual trajectory 140 is between the two trajectory boundaries 130; If so, the output angle deviation E is 0; If not, the angle between the initial segment actual trajectory 140 and the adjacent trajectory boundary 130 is obtained, and the angle is output as the angle deviation E.

[0039] In this embodiment, when a player cheats by touching the cue ball with a micro-movement of his hand, the moving angle and speed of the cue ball will change. At the same time, considering that if the cue ball is used as a reference object, it is difficult to track the change of the initial segment trajectory of the cue ball when the distance between the cue ball and the hit ball is very close, so the hit ball is used as a reference object here. When there is an abnormality in the movement trajectory of the cue ball, the hit ball will also be abnormal, and the hit ball generally has obvious changes in the initial segment trajectory. Here, the hitting state parameters of the target player (including the hitting angle, the action amplitude of the billiard cue, the speed, etc.) can be obtained based on the monitoring image, so as to predict the initial segment theoretical trajectory of the hit ball. Considering the calculation error, two trajectory boundaries 130 are set here to represent the deviation range of different directions of the initial segment theoretical trajectory. If the initial segment actual trajectory 140 of the hit ball is between the two trajectory boundaries 130, it can be indicated that there is no suspicion of cheating, that is, the output angle deviation E is 0. If it is not between the two trajectory boundaries 130, it indicates that there is suspicion of cheating. At this time, the angle between the initial segment actual trajectory 140 and the adjacent trajectory boundary 130 (that is, Figure 3 The output of the included angle θ is the angle deviation E, and the calculation is accurate and reliable.

[0040] As an optional implementation, identifying the image information of the target player in the monitoring image to obtain the behavior abnormality degree R of the target player includes: Identify the image information of the target player in the monitoring image; Compare the target player's image information with a preset image database to obtain the abnormal behavior index R1 and the abnormal micro-expression index R2; wherein the image database stores the image information of billiards game behavior norms; According to the abnormal behavior index R1 and the abnormal micro-expression index R2, the abnormal behavior degree R is obtained; wherein R=R1+R2.

[0041] In this embodiment, when calculating the behavior abnormality R, the two indicators of behavior abnormality index R1 and micro-expression abnormality index R2 are comprehensively considered. Here, the existing similarity algorithm can be combined to compare the target player image information with the image database, and R1 and R2 are calculated respectively according to the comparison difference. The sum of the two is the behavior abnormality R, so that the behavior abnormality R can be accurately quantified and calculated.

[0042] As an optional implementation, according to the billiard cue posture information, theoretical trajectory information is obtained from the historical game database of the target player, including: Get the current billiards game type of the target player to obtain the historical game database of the same type; Inputting the billiard cue posture information into the historical game database and performing similarity association to determine whether associated trajectory information matching the input billiard cue posture information can be found; If so, the matched associated trajectory information is output as theoretical trajectory information; If not, the cue stick posture information is input into a preset trajectory prediction model to obtain theoretical trajectory information.

[0043] In this embodiment, when obtaining theoretical trajectory information, the current billiard game type is first identified to match the historical game database of the same type participated by the target player. If no match is found, it means that the target player is participating in this type of billiard game for the first time. At this time, the historical game data of multiple relatively standard billiard players and the general historical game database obtained based on the big data algorithm can be combined as the current matching database. After the billiard cue posture information is input into the historical game database, similarity association can be performed to determine whether associated trajectory information matching the input billiard cue posture information can be found. If so, the matched associated trajectory information is output as the theoretical trajectory information. If not, the billiard cue posture information is input into the trained trajectory prediction model to predict the corresponding theoretical trajectory information, thereby ensuring the accuracy of the basic information. Embodiment 2

[0044] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a billiards game data intelligent monitoring system, including: A video acquisition module is used to acquire monitoring video of a target billiards game area; An image decomposition module is used to decompose the monitoring video frame by frame to obtain multiple monitoring images; An image recognition module is used to obtain the target player's billiard cue posture information and the actual trajectory information of the target billiard ball according to a plurality of monitoring images; wherein the billiard cue posture information includes the billiard cue's movement amplitude, displacement speed and hitting angle, and the target billiard ball includes the cue ball and the hit ball; An information matching module is used to obtain theoretical trajectory information from a historical match database of a target player according to the billiard cue posture information; wherein the historical match database stores analysis data of the target player's billiard matches of the same type; The first data processing module is used to determine whether the actual trajectory information matches the theoretical trajectory information. If so, it returns to obtain the monitoring video of the target billiards competition area. If not, it obtains multiple abnormal factors based on the monitoring image; wherein the abnormal factors are environmental state parameters when the target player cheats; A cheating assessment module, used to input multiple abnormal factors into a preset assessment model to obtain a cheating assessment value; The second data processing module is used to determine whether the cheating evaluation value is greater than a preset standard value. If so, it outputs cheating confirmation information; if not, it returns to obtaining the monitoring video of the target billiards competition area.

[0045] The relevant explanations and examples of each module in the device of this embodiment can refer to the method of the aforementioned embodiment, and will not be repeated here. Embodiment 3

[0046] Based on the same inventive concept as the above-mentioned embodiment, this embodiment provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned method. Embodiment 4

[0047] Based on the same inventive concept as the aforementioned embodiment, this embodiment provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the aforementioned method.

[0048] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for intelligently monitoring billiards game data, characterized in that: The following steps are involved: Obtain monitoring video of the target billiards game area; Decompose the monitoring video frame by frame to obtain multiple monitoring images; According to the multiple monitoring images, the target player's billiard cue posture information and the actual trajectory information of the target billiard ball are obtained; wherein the billiard cue posture information includes the motion amplitude, displacement speed and hitting angle of the billiard cue, and the target billiard ball includes the cue ball and the hit ball; According to the billiard cue posture information, theoretical trajectory information is obtained from a historical match database of a target player; wherein the historical match database stores analysis data of the target player's billiard matches of the same type; Determine whether the actual trajectory information matches the theoretical trajectory information. If so, return to obtaining the monitoring video of the target billiards competition area. If not, obtain multiple abnormal factors based on the monitoring image; wherein the abnormal factors are state parameters of the target player when cheating; Inputting multiple abnormal factors into a preset evaluation model to obtain a cheating evaluation value; Determine whether the cheating evaluation value is greater than a preset standard value. If so, output cheating confirmation information. If not, return to obtaining the monitoring video of the target billiards competition area.

2. A method for intelligently monitoring billiards game data as claimed in claim 1, characterized in that: Based on the monitoring images, multiple abnormal factors are obtained, including: Identify the target player's image information in the monitoring image to obtain the target player's behavior abnormality R; wherein the behavior abnormality R represents the abnormality of the target player's behavior and micro-expression; According to the monitoring image, the trajectory difference G between the actual trajectory information and the theoretical trajectory information is obtained; wherein the trajectory difference G represents the difference between the actual trajectory travel of the cue ball and the hit ball and the theoretical trajectory travel; According to the monitoring image, the angle deviation E of the hit ball is obtained; wherein the angle deviation E represents the angle deviation between the actual trajectory of the initial segment of the hit ball and the theoretical trajectory, and the actual trajectory of the initial segment is the actual movement trajectory of the hit ball from the time of being hit to the next collision time or the time of stopping.

3. A method for intelligently monitoring billiards game data as claimed in claim 2, characterized in that: The expression for evaluating the model is: Q=(n+1) (K1) R+K2 G+K3 E); In the formula, Q is the cheating evaluation value, n is the target player's historical cheating times, K1 is the first conversion coefficient, K2 is the second conversion coefficient, and K3 is the third conversion coefficient.

4. A method for intelligently monitoring billiards game data as claimed in claim 2 or 3, characterized in that: The theoretical trajectory information includes the cue ball theoretical trajectory travel and the hit ball theoretical trajectory travel, both of which are range values; According to the monitoring image, the trajectory difference G between the actual trajectory information and the theoretical trajectory information is obtained, including: According to the monitoring image, the actual trajectory of the cue ball and the actual trajectory of the hit ball are obtained; Compare the actual trajectory of the cue ball with the theoretical trajectory of the cue ball to obtain a first trajectory difference G1; Compare the actual trajectory of the hit ball with the theoretical trajectory of the hit ball to obtain a second trajectory difference G2; According to the first trajectory difference G1 and the second trajectory difference G2, the trajectory difference G is obtained; wherein G=W1 G1+W2 G2, W1 is the first weight value, W2 is the second weight value, and W1<W2.

5. A method for intelligently monitoring billiards game data as claimed in claim 4, characterized in that: Comparing the actual trajectory of the cue ball with the theoretical trajectory of the cue ball to obtain a first trajectory difference G1 includes: Determine whether the actual trajectory of the cue ball is within the range of the theoretical trajectory of the cue ball; If yes, then the output first trajectory difference G1 is 0; If not, then obtain a first difference between the maximum or minimum value of the actual trajectory of the cue ball and the theoretical trajectory of the cue ball, and output the first difference as a first trajectory difference G1.

6. A method for intelligently monitoring billiards game data as claimed in claim 4, characterized in that: Comparing the actual trajectory of the ball being hit with the theoretical trajectory of the ball being hit to obtain a second trajectory difference G2, including: Determine whether the actual trajectory of the ball being hit is within the range of the theoretical trajectory of the ball being hit; If yes, then the output second trajectory difference G2 is 0; If not, then obtain a second difference between the maximum or minimum value of the actual trajectory of the hit ball and the theoretical trajectory of the hit ball, and output the second difference as a second trajectory difference G2.

7. A method for intelligently monitoring billiards game data as claimed in claim 2 or 3, characterized in that: According to the monitoring image, the angle deviation E of the ball being hit is obtained, including: According to the monitoring image, the batting state parameters of the target player are obtained to obtain the initial segment theoretical trajectory of the batted ball; wherein the initial segment theoretical trajectory is the theoretical moving trajectory of the batted ball from the moment of being hit to the next collision moment or the stopping moment, and the initial segment theoretical trajectory has two trajectory boundaries; According to the monitoring image, the actual trajectory of the initial segment of the ball being hit is obtained; Determine whether the actual trajectory of the initial segment is between the boundaries of the two trajectories; If so, the output angle deviation E is 0; If not, the angle between the actual trajectory of the initial segment and the adjacent trajectory boundary is obtained, and the angle is output as the angle deviation E.

8. A method for intelligently monitoring billiards game data as claimed in claim 2 or 3, characterized in that: Identify the target player image information in the monitoring image to obtain the target player's behavior abnormality R, including: Identify the image information of the target player in the monitoring image; Compare the target player's image information with a preset image database to obtain the abnormal behavior index R1 and the abnormal micro-expression index R2; wherein the image database stores the image information of billiards game behavior norms; According to the abnormal behavior index R1 and the abnormal micro-expression index R2, the abnormal behavior degree R is obtained; wherein R=R1+R2.

9. A method for intelligently monitoring billiards game data as claimed in claim 1, characterized in that: According to the cue stick posture information, theoretical trajectory information is obtained from the historical game database of the target player, including: Get the current billiards game type of the target player to obtain the historical game database of the same type; Inputting the billiard cue posture information into the historical game database and performing similarity association to determine whether associated trajectory information matching the input billiard cue posture information can be found; If so, the matched associated trajectory information is output as theoretical trajectory information; If not, the cue stick posture information is input into a preset trajectory prediction model to obtain theoretical trajectory information.

10. A billiards game data intelligent monitoring system, characterized in that: include: A video acquisition module is used to acquire monitoring video of a target billiards game area; An image decomposition module is used to decompose the monitoring video frame by frame to obtain multiple monitoring images; An image recognition module is used to obtain the target player's billiard cue posture information and the actual trajectory information of the target billiard ball according to a plurality of monitoring images; wherein the billiard cue posture information includes the billiard cue's movement amplitude, displacement speed and hitting angle, and the target billiard ball includes the cue ball and the hit ball; An information matching module is used to obtain theoretical trajectory information from a historical match database of a target player according to the billiard cue posture information; wherein the historical match database stores analysis data of the target player's billiard matches of the same type; The first data processing module is used to determine whether the actual trajectory information matches the theoretical trajectory information. If so, it returns to obtain the monitoring video of the target billiards competition area. If not, it obtains multiple abnormal factors based on the monitoring image; wherein the abnormal factors are environmental state parameters when the target player cheats; A cheating assessment module, used to input multiple abnormal factors into a preset assessment model to obtain a cheating assessment value; The second data processing module is used to determine whether the cheating evaluation value is greater than a preset standard value. If so, it outputs cheating confirmation information; if not, it returns to obtaining the monitoring video of the target billiards competition area.

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