Self-testing method and system for position of camera shooting unmanned aerial vehicle in stadium

Abstract

The invention provides a stadium camera shooting unmanned aerial vehicle position self-testing method and system, and the method comprises the steps: extracting a frame image from a shot video during the real-time shooting of a stadium video during the flight of an unmanned aerial vehicle; performing feature extraction on the image to obtain image feature data of the stadium; calculating relative position data of the unmanned aerial vehicle and the stadium based on the image feature data of the stadium; and the unmanned aerial vehicle detects whether the unmanned aerial vehicle deviates from a preset position range based on the relative position data. On the premise that the environment is not modified and any additional equipment is installed, and under the condition that third-party positioning is not needed, self-sensing of the position is achieved through images shot by the unmanned aerial vehicle, so that wrong instructions are avoided, damage is avoided, and the method is particularly suitable for scene application of indoor sports events.

Description

Technical Field

[0001] The present invention relates to the technical fields of unmanned aerial vehicles (UAVs) and sports event image processing, and in particular to a method and system for self-detecting the position of a video-taking UAV at a sports venue. Background Art

[0002] In live sports broadcasts, fixed camera positions are often limited. Therefore, many live events are using drones to film sports without disrupting the athletes' performance. However, in indoor environments, even with a dedicated pilot, obstructions, signal interference, and other factors can cause the drone to erroneously respond to commands, potentially leading to property damage. Therefore, a drone should have a self-detection function. Without any modifications to the environment or the installation of additional equipment, the drone can calculate its position, thereby avoiding erroneous commands and preventing damage. However, in many indoor stadiums, Beidou or GPS signals are weak or inaccurate, making third-party positioning impossible. The drone must rely on its own facilities and capabilities for detection. Summary of the Invention

[0003] Based on the above records, the present invention provides a method and system for self-detecting the position of a sports venue camera drone, aiming to solve the technical problem in the prior art that drones are unable to self-monitor and locate.

[0004] The present invention provides a method for self-detecting the position of a sports venue camera drone, comprising:

[0005] Step A1, when the drone is flying and shooting a video of the sports venue in real time, extracting a frame image from the shot video;

[0006] Step A2, extracting features from the image to obtain image feature data of the sports venue;

[0007] Step A3, calculating relative position data between the UAV and the sports venue based on the image feature data of the sports venue;

[0008] In step A4, the drone detects whether the drone deviates from a preset position range based on the relative position data.

[0009] Furthermore, in step A4, if it is detected that the drone deviates from the preset position range, step A5 is executed; if it is detected that the drone does not deviate from the preset position range, step A6 is executed after receiving the drone driving control instruction;

[0010] Step A5: upon receiving the drone driving control instruction, determine whether to execute the drone driving control instruction:

[0011] If yes, proceed to step A6; if no, do not execute the drone driving control command;

[0012] Step A6: Execute the received UAV driving control instruction.

[0013] Furthermore, before step A1, the method further includes step A0: inputting actual size data of the sports field, wherein the actual size data of the sports field includes the actual sizes of the baselines at both ends, the center line, and the side lines on both sides;

[0014] In step A2, the image feature data of the sports field includes the pixel lengths of the baselines at both ends and the center line of the sports field;

[0015] In step A3, the relative position data between the drone and the sports venue is calculated based on the actual size data and image feature data of the sports venue.

[0016] Furthermore, the relative position data includes the relative distance between the drone and the central axis of the site, and the preset position range includes a preset distance range between the drone and the central axis of the site;

[0017] In step A4, when it is detected that the relative distance between the drone and the central axis of the venue is outside the preset distance range between the drone and the central axis of the venue, it is determined that the drone has deviated from the preset position range.

[0018] Furthermore, the relative position data includes the relative distance between the drone and the center point of the site, and the preset position range includes a preset distance range between the drone and the center point of the site;

[0019] In step A4, when it is detected that the relative distance between the drone and the center point of the field is outside the preset distance range between the drone and the center point of the field, it is determined that the drone has deviated from the preset position range.

[0020] The present invention provides a self-detection system for a sports venue camera drone position, which is applied to a drone and is used to perform the aforementioned sports venue camera drone position self-detection method, comprising:

[0021] The image acquisition module is used to extract frame images from the video captured by the drone in real time during the flight of the sports venue;

[0022] The feature extraction module is connected to the image acquisition module and is used to extract features from the image to obtain image feature data of the sports venue;

[0023] A position calculation module, connected to the feature extraction module, is used to calculate the relative position data of the UAV and the sports venue based on the image feature data of the sports venue;

[0024] The deviation detection module is connected to the position calculation module and is used to detect whether the drone deviates from the preset position range based on the relative position data.

[0025] Furthermore, the self-test system also includes:

[0026] The command receiving module is used to receive the UAV driving control commands;

[0027] The execution judgment module is connected to the deviation detection module and the instruction receiving module respectively, and is used to: when it is detected that the drone deviates from the preset position range, determine whether to execute the received drone driving control instruction and obtain a judgment result;

[0028] The instruction execution module is respectively connected to the deviation detection module, the instruction receiving module and the execution judgment module, and is used to: execute the drone driving control instruction when the judgment result is execution, not execute the drone driving control instruction when the judgment result is not execution, and execute the received drone driving control instruction when it is detected that the drone has not deviated from the preset position range.

[0029] Furthermore, the self-test system further comprises a feature input module: for inputting actual size data of the sports field, the actual size data of the sports field including the actual sizes of the baselines at both ends, the center line, and the side lines on both sides;

[0030] The image feature data of the sports field includes the pixel lengths of the baselines at both ends and the center line of the sports field;

[0031] The position calculation module is also connected to the feature input module and is used to calculate the relative position data of the drone and the sports venue based on the actual size data and image feature data of the sports venue.

[0032] Furthermore, the relative position data includes the relative distance between the drone and the central axis of the site, and the preset position range includes a preset distance range between the drone and the central axis of the site;

[0033] The deviation detection module determines that the drone has deviated from the preset position range when detecting that the relative distance between the drone and the central axis of the site is outside the preset distance range between the drone and the central axis of the site.

[0034] Furthermore, the relative position data includes the relative distance between the drone and the center point of the site, and the preset position range includes a preset distance range between the drone and the center point of the site;

[0035] The deviation detection module determines that the drone has deviated from the preset position range when detecting that the relative distance between the drone and the center point of the site is outside the preset distance range between the drone and the center point of the site.

[0036] The beneficial technical effect of the present invention is that, without any modification to the environment or installation of any additional equipment, and without the need for third-party positioning, the image captured by the drone can be used to achieve self-perception of the position, thereby avoiding erroneous instructions and avoiding injuries. The invention is particularly suitable for application in indoor sports events. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is the principle diagram of camera imaging;

[0038] Figure 2 This is a cross-sectional diagram of the camera position and the playing field.

[0039] Figure 3-4 This is a flowchart of the steps of a method for self-detecting the position of a sports venue camera drone according to the present invention;

[0040] Figure 5 This is a module schematic diagram of a self-detection system for a sports venue camera drone position according to the present invention; DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0042] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0043] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0044] See also Figure 3 The present invention provides a method for self-detecting the position of a sports venue camera drone, comprising:

[0045] Step A1, when the drone is flying and shooting a video of the sports venue in real time, extracting a frame image from the shot video;

[0046] Step A2, extracting features from the image to obtain image feature data of the sports venue;

[0047] Step A3, calculating relative position data between the UAV and the sports venue based on the image feature data of the sports venue;

[0048] In step A4, the drone detects whether the drone deviates from a preset position range based on the relative position data.

[0049] For a formal competition venue, the location and size of the playing field are typically fixed, while the position of the camera (on the drone) is constantly changing based on the needs of the event. Analyzing these variations reveals that drone footage typically encompasses the entire field, so the absolute position of the camera is less important than the relative position of the camera (and, in turn, the drone's relative position). Because the shape and pitch angle of the playing field can vary significantly from location to location, calculating the drone's position is crucial.

[0050] The present invention involves using a drone camera to regularly capture images of indoor sports venues, extracting appropriate frames from them and converting them into images. The captured images are then analyzed and calculated using an algorithm, ultimately determining the user's relative position based on the calculation results. This relative position is then used to determine whether the user's position is safe and whether it has an impact on the players on the field. Without any modifications to the environment or installation of any additional equipment, and without the need for third-party positioning, the drone's captured images are used to achieve self-awareness of position, thereby avoiding erroneous instructions and preventing injury. The system is particularly suitable for use in indoor sports events.

[0051] See also Figure 4 , further, in step A4, if it is detected that the drone deviates from the preset position range, step A5 is executed; if it is detected that the drone does not deviate from the preset position range, step A6 is executed after receiving the drone driving control instruction;

[0052] Step A5: upon receiving the drone driving control instruction, determine whether to execute the drone driving control instruction:

[0053] If yes, proceed to step A6; if no, do not execute the drone driving control command;

[0054] Step A6: Execute the received UAV driving control instruction.

[0055] The drone performs self-position detection. After receiving a drone control command, it executes it if it deems the command valid, but not if it deems it an error. This prevents erroneous actions caused by position deviation, thereby circumventing erroneous commands and preventing injuries. In many indoor stadiums, Beidou or GPS signals are poor or inaccurate. This invention allows for positioning using the drone's own facilities and functions when third-party positioning is unavailable.

[0056] Furthermore, before step A1, the method further includes step A0: inputting actual size data of the sports field, wherein the actual size data of the sports field includes the actual sizes of the baselines at both ends, the center line, and the side lines on both sides;

[0057] In step A2, the image feature data of the sports field includes the pixel lengths of the baselines at both ends and the center line of the sports field;

[0058] In step A3, the relative position data between the drone and the sports venue is calculated based on the actual size data and image feature data of the sports venue.

[0059] Before the game, you should know the size of the playing field where you will be testing yourself. The standard sizes of some common sports venues are shown in the following table:

[0060]

[0061] It can be considered that for these basketball, football, tennis, and badminton courts, the actual dimensions of the baselines and center lines are the width of the court, and the actual dimensions of the sidelines are the length of the court. Furthermore, the relative position data includes the relative distance between the drone and the center axis of the court, and the preset position range includes the preset distance range between the drone and the center axis of the court.

[0062] In step A4, when it is detected that the relative distance between the drone and the central axis of the venue is outside the preset distance range between the drone and the central axis of the venue, it is determined that the drone has deviated from the preset position range;

[0063] Among them, the central axis of the field refers to a line passing through the baselines at both ends and the midpoint of the center line.

[0064] Furthermore, the relative position data includes the relative distance between the drone and the center point of the site, and the preset position range includes a preset distance range between the drone and the center point of the site;

[0065] In step A4, when it is detected that the relative distance between the drone and the center point of the field is outside the preset distance range between the drone and the center point of the field, it is determined that the drone has deviated from the preset position range.

[0066] Most sports venues are rectangular, and the center point of the venue can also be considered as the intersection of the venue's central axis and the venue's center line, that is, the center point of the center line.

[0067] The relative position data mainly includes the relative distance between the camera and the center point of the venue and the relative distance between the camera and the central axis of the venue, that is, the distance between the camera and the center point of the venue and the distance between the camera and the central axis of the venue.

[0068] W is the actual size of object A (for example, the actual length of the bottom line where the goal is located on a football field), P is the pixel size of object A in the image captured by the camera, f is the focal length, and the unit is the same as P, and L is the distance from the center of the convex lens of the camera on the drone to the center of mass of object A. Then, Figure 1 As shown, the imaging principle is described as: P / f=W / L.

[0069] Assuming the playing field is a rectangle, if we make a Cartesian coordinate system on the rectangle, and the x-axis and y-axis are parallel to the sides of the rectangle, and the origin of the coordinate system is at the exact center of the rectangle, which is also the center of the field, then its units will be the actual measured units of the playing field.

[0070] Without losing generality, let's take a football field as an example. Suppose the drone is hovering above a certain position in the stands behind one of the football goals. Then, although the lines where the two football goals are located (i.e., the baselines at both ends) and the line in the middle (i.e., the center line of the field) are parallel and equal in length in reality, due to the perspective problem, they are not necessarily parallel or equal in length in the image. In reality, we can imagine a line that passes through the exact midpoints of these three lines (called the center axis of the field). This line extends infinitely to both ends, so no matter where the drone is, you can draw a perpendicular line to the center axis. If a camera wants to shoot an entire field, it needs to have several obvious characteristics at the same time. That is, the camera usually needs to be outside the rectangular area of the field, and the camera position must be higher than the plane where the above-mentioned Cartesian coordinate system is located. If a perpendicular line is drawn from the camera position to the center axis of the field, the generated cross-section passes through the field, and its cross-section is as follows. Figure 2 shown.

[0071] As an implementation method, Figure 2 As shown in the figure, the "camera position" refers to the location where the drone is placed (more specifically, the focal point of the drone's camera lens). The line "L0 - tt" represents the centerline of the field. Therefore, H is the distance between the camera (i.e., the drone) and the centerline. If the drone is directly above the centerline, H is the actual height of the drone relative to the field. If the drone deviates from the centerline, H is greater than the drone's actual height. The greater the deviation, the greater the discrepancy between H and the drone's actual height. The distance between the first line on the field (the baseline closest to the drone) and the plane perpendicular to the field's centerline is L0. The distance between the second line on the field (the centerline) and the plane perpendicular to the field's centerline is L0 + t1. The distance between the third line on the field (the baseline for drones) and the plane perpendicular to the field's centerline is L0 + t1 + t2. Since the distances from the centerline to the baselines at both ends are equal, t1 = t2 = t, and 2t is the length of the field, i.e., the actual dimension of the sidelines, i.e., the distance between the baselines. The distance between the bottom line, the center line and the plane perpendicular to the center axis of the field can be expressed as the intersection between the center axis of the field and the plane perpendicular to the center axis of the field ( Figure 2 The distance between point O in the middle of the bottom line and the middle point of the center line.

[0072] The distance between point O and the camera position is H, the distance between the camera position and the center point of the first baseline (point B) is L1, the distance between the camera position and the center point (point C) is L2, and the distance between the camera position and the center point of the second baseline (point D) is L3. Most competition venues are rectangular, so the two baselines and the center line are of equal length, for example, W. Based on the principles of geometry and imaging, the following equations exist:

[0073]

[0074] Where P1 is the pixel length of the first baseline in the image of the sports field captured by the camera, P2 is the pixel length of the center line in the image of the sports field captured by the camera, and P3 is the pixel length of the second baseline in the image of the sports field captured by the camera. The baseline and center lines in the image can be identified using an artificial intelligence algorithm, and P1, P2, and P3 can be obtained by identifying the baseline and center lines using the artificial intelligence algorithm.

[0075] If point O is 0, as shown in the figure, point O is between points B and C, then L0 is the distance from point O to point B. L0 is a negative value, L0+t1 is equivalent to the distance from point O to point C, and L0+t1+t2 is also equivalent to the distance from point O to point D.

[0076] The two baselines and the center line form three parallel lines of equal length, which are easy to find on a sports field. Since the center line is located exactly in the middle of the two baselines, the distance from the center line to the two baselines is also equal (this is t in the equation). For formal sports competitions, the venues are generally standard, so the length and width of the venue are fixed. This means that W and t in the above equations are constants or known values.

[0077] In the equation group, the focal length f, the relative distance H between the drone and the center axis of the field, and the distance L0 between the first baseline on the sports field and the plane perpendicular to the center axis of the field of the drone are all variables. The three variables can be solved by the above equation group, for example, as shown below:

[0078]

[0079] According to the calculation results of the three variables f, H and L0, the values of L1, L2 and L3 can be obtained as follows:

[0080] L1=H 2 +L0 2

[0081] L2=H 2 +(L0+t) 2

[0082] L3=H 2+(L0+2t) 2

[0083] It should be noted that the above formula is only one of the results obtained after solving the system of equations. Different calculation expressions can be obtained by different derivation orders, but this does not affect the calculation results.

[0084] The L2 value in the display can indicate how far the drone is from the center of the venue. If it is larger, it means that the drone is flying too high or too far, which may affect the audience seats, etc. A smaller value means that it is closer to the venue.

[0085] By combining H and L2, it can be determined whether the drone has deviated.

[0086] Furthermore, in step A3, an artificial intelligence algorithm is used to calculate the relative position data between the drone and the sports venue.

[0087] When a drone is shooting video, it can extract frames from the video stream at predetermined intervals, such as once a second, to determine the drone's position deviation. Using an AI algorithm, it can identify the baseline and center line of the sports field in the image, calculate P1, P2, and P3, and then substitute the known data W, t, P1, P2, and P3 into the equations to obtain f, H, and L0, which facilitates the calculation of L1, L2, and L3. This allows the drone to be judged as being in an excessively large angular deviation (H value too high or too low) or in an unfavorable environment (L2 value too high).

[0088] Through this method, the drone camera has a self-protection function, thereby avoiding unnecessary injuries during work.

[0089] See also Figure 5 The present invention also provides a self-detection system for a sports venue camera drone position, which is applied to a drone and is characterized in that it is used to perform the aforementioned sports venue camera drone position self-detection method, comprising:

[0090] An image acquisition module (1) is used to extract frame images from a video captured by the drone in real time during flight.

[0091] A feature extraction module (2) is connected to the image acquisition module (1) and is used to extract features from the image to obtain image feature data of the sports venue;

[0092] A position calculation module (3) is connected to the feature extraction module (2) and is used to calculate relative position data between the UAV and the sports venue based on the image feature data of the sports venue;

[0093] The deviation detection module (4) is connected to the position calculation module (3) and is used for the drone to detect whether the drone deviates from a preset position range based on relative position data.

[0094] Furthermore, the self-test system also includes:

[0095] A command receiving module (5) is used to receive UAV driving control commands;

[0096] An execution judgment module (7) is connected to the deviation detection module (4) and the instruction receiving module (5) respectively, and is used to: when detecting that the drone deviates from the preset position range, determine whether to execute the received drone driving control instruction and obtain a judgment result;

[0097] The instruction execution module (8) is connected to the deviation detection module (4), the instruction receiving module (5) and the execution judgment module (7) respectively, and is used to: execute the drone driving control instruction when the judgment result is execution, not execute the drone driving control instruction when the judgment result is not execution, and execute the received drone driving control instruction when it is detected that the drone has not deviated from the preset position range. Furthermore, the self-test system also includes a feature input module (6): used to input the actual size data of the sports field, the actual size data of the sports field including the actual size of the bottom line at both ends, the center line, and the side lines on both sides;

[0098] The image feature data of the sports field includes the pixel lengths of the baselines at both ends and the center line of the sports field;

[0099] The position calculation module (3) is also connected to the feature input module (6) and is used to calculate the relative position data between the drone and the sports field based on the actual size data and image feature data of the sports field.

[0100] Furthermore, the relative position data includes the relative distance between the drone and the central axis of the site, and the preset position range includes a preset distance range between the drone and the central axis of the site;

[0101] The deviation detection module (4) determines that the drone has deviated from the preset position range when detecting that the relative distance between the drone and the center axis of the site is outside the preset distance range between the drone and the center axis of the site;

[0102] Among them, the central axis of the field refers to a line passing through the baselines at both ends and the midpoint of the center line.

[0103] Furthermore, the relative position data includes the relative distance between the drone and the center point of the site, and the preset position range includes a preset distance range between the drone and the center point of the site;

[0104] The deviation detection module (4) determines that the drone has deviated from the preset position range when detecting that the relative distance between the drone and the center point of the field is outside the preset distance range between the drone and the center point of the field.

[0105] The above are only preferred embodiments of the present invention and do not limit the implementation methods and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for self-detecting the position of a sports venue camera drone, characterized in that: include: Step A1, extracting frame images from the video captured by the drone in real time during flight; Step A2, performing feature extraction on the image to obtain image feature data of the sports venue; Step A3, calculating relative position data between the drone and the sports venue based on the image feature data of the sports venue; In step A4, the drone detects whether the drone deviates from a preset position range based on the relative position data.

2. A method for self-detecting the position of a sports venue camera drone according to claim 1, characterized in that: In step A4, if it is detected that the drone deviates from the preset position range, step A5 is executed; if it is detected that the drone does not deviate from the preset position range, step A6 is executed after receiving the drone driving control instruction; Step A5: upon receiving the drone driving control instruction, determine whether to execute the drone driving control instruction: If yes, proceed to step A6; if no, do not execute the drone driving control instruction; Step A6: execute the received UAV driving control instruction.

3. The method for self-detecting the position of a sports venue camera drone according to claim 1, wherein: Before step A1, the method further includes step A0: inputting actual size data of the sports field, wherein the actual size data of the sports field includes actual sizes of the baselines at both ends, the center line, and the side lines on both sides; In step A2, the image feature data of the sports field includes the pixel lengths of the baselines at both ends and the center line of the sports field; In step A3, the relative position data between the drone and the sports venue is calculated based on the actual size data and image feature data of the sports venue.

4. A method for self-detecting the position of a sports venue camera drone according to claim 3, characterized in that: The relative position data includes the relative distance between the drone and the center axis of the venue, and the preset position range includes the preset distance range between the drone and the center axis of the venue; In step A4, when it is detected that the relative distance between the drone and the central axis of the venue is outside the preset distance range between the drone and the central axis of the venue, it is determined that the drone has deviated from the preset position range.

5. The method for self-detecting the position of a sports venue camera drone according to claim 3, wherein: The relative position data includes the relative distance between the drone and the center point of the site, and the preset position range includes the preset distance range between the drone and the center point of the site; In step A4, when it is detected that the relative distance between the drone and the center point of the field is outside the preset distance range between the drone and the center point of the field, it is determined that the drone has deviated from the preset position range.

6. A self-detection system for the position of a sports field camera drone, applied to a drone, characterized in that: A method for self-detecting the position of a sports venue camera drone according to any one of claims 1 to 5, comprising: An image acquisition module is used to extract frame images from the video captured by the drone in real time during flight. a feature extraction module, connected to the image acquisition module, for performing feature extraction on the image to obtain image feature data of the sports venue; a position calculation module, connected to the feature extraction module, for calculating relative position data between the drone and the sports venue based on the image feature data of the sports venue; A deviation detection module is connected to the position calculation module and is used for the drone to detect whether the drone deviates from a preset position range based on the relative position data.

7. A sports venue camera drone position self-detection system according to claim 6, characterized in that: The self-test system further comprises: The command receiving module is used to receive the UAV driving control commands; an execution judgment module, connected to the deviation detection module and the instruction receiving module respectively, and configured to: upon detecting that the UAV deviates from the preset position range, determine whether to execute the received UAV driving control instruction and obtain a judgment result; The instruction execution module is respectively connected to the deviation detection module, the instruction receiving module and the execution judgment module, and is used to: execute the drone driving control instruction when the judgment result is execution, not execute the drone driving control instruction when the judgment result is not execution, and execute the received drone driving control instruction when it is detected that the drone has not deviated from the preset position range.

8. A sports venue camera drone position self-detection system as claimed in claim 6, characterized in that: The self-test system further comprises a feature input module: for inputting actual size data of the sports field, wherein the actual size data of the sports field includes actual sizes of the baselines at both ends, the center line, and the side lines on both sides; The image feature data of the sports field includes the pixel lengths of the baselines at both ends and the center line of the sports field; The position calculation module is also connected to the feature input module and is used to calculate the relative position data of the drone and the sports venue based on the actual size data and image feature data of the sports venue.

9. A sports venue camera drone position self-detection system as claimed in claim 8, characterized in that: The relative position data includes the relative distance between the drone and the center axis of the venue, and the preset position range includes the preset distance range between the drone and the center axis of the venue; The deviation detection module determines that the drone has deviated from the preset position range when detecting that the relative distance between the drone and the central axis of the venue is outside the preset distance range between the drone and the central axis of the venue.

10. A sports venue camera drone position self-detection system according to claim 8, characterized in that: The relative position data includes the relative distance between the drone and the center point of the site, and the preset position range includes the preset distance range between the drone and the center point of the site; The deviation detection module determines that the drone has deviated from the preset position range when detecting that the relative distance between the drone and the center point of the field is outside the preset distance range between the drone and the center point of the field.

Images