Sighting point track measuring method of sighting telescope

Abstract

The invention discloses a sighting point track measuring method of a sighting telescope. The method comprises the following steps: acquiring at least one sighting scene image comprising a light spot position on a target; for each aiming scene image in the at least one aiming scene image, intercepting a region of interest in each aiming scene image according to the proportion of the light spot pixel number in each aiming scene image to obtain at least one region of interest; and determining the aiming point track according to the at least one region of interest and the firing action of the shooting body. According to the method, the region of interest is dynamically determined according to the proportion of the number of light spot pixels in the first aiming scene image, precise matching with target features can be self-adapted, further, row-column accumulation operation is performed on pixels higher than the proportion, background interference is effectively inhibited, track core features are reserved, and meanwhile, the accuracy of the target features is improved. And the redundant calculation amount is greatly reduced.

Description

Technical Field

[0001] This application relates to the field of shooting training technology, and in particular to a method for measuring the aiming point trajectory of a scope. Background Technology

[0002] Compared to simulated shooting training, live-fire shooting training provides trainees with a realistic shooting experience, which helps improve training effectiveness. In live-fire shooting training, evaluating shooting skills is crucial for improving training results. Accurate evaluation helps identify shortcomings in the shooter's training process, allowing for targeted improvements to training methods.

[0003] Shooting skill is related to the trainee's shooting movements, and the aiming point trajectory reflects the trainee's mastery of these movements. For long-range sniping training, image processing is needed to determine the aiming point trajectory. Due to limitations in computing resources and environmental interference during outdoor shooting training, the real-time performance and accuracy of the aiming point trajectory still need further improvement. Summary of the Invention

[0004] This application provides a method for measuring the aiming point trajectory of a scope, which improves the real-time performance and accuracy of the aiming point trajectory and helps to enhance shooting training effectiveness.

[0005] In a first aspect, a method for measuring the aiming trajectory of a sight is provided. This method can be executed by an electronic device with image processing capabilities, which is mounted behind the sight of a firing body. The method includes: acquiring at least one aiming scene image including the position of a spot on a target; for each of the at least one aiming scene images, cropping a region of interest in each aiming scene image according to the proportion of the number of pixels of the spot in each aiming scene image to obtain at least one region of interest; and determining the aiming trajectory based on the at least one region of interest and the firing action of the firing body.

[0006] In this method, the region of interest is dynamically determined based on the proportion of the number of light spot pixels in the first aiming scene image, which can adaptively and accurately match the target features. Furthermore, for pixels with a proportion higher than this, row and column accumulation operations are performed, which effectively suppresses background interference, preserves the core features of the trajectory, and significantly reduces redundant computation.

[0007] In one implementation, the method further includes: sorting the grayscale histogram corresponding to each aiming scene image from low to high according to the grayscale value, and calculating the cumulative pixel value; determining the largest value among all grayscale values ​​whose cumulative pixel value exceeds the number of spot pixels as the proportion of the number of spot pixels in each aiming scene image.

[0008] In one implementation, extracting the region of interest (ROI) from each aiming scene image based on the proportion of light spot pixels in each aiming scene image includes: during the image data input process of each aiming scene image, the pixel values ​​of each row are accumulated in real time, and the current row accumulation result is compared with the proportion using a comparator. The row coordinates corresponding to the maximum value output by the comparator are determined as the center row coordinates of the ROI; the image data is allocated, and after a row of pixels is allocated, it is accumulated bit by bit into the buffer until the last pixel of a frame ends, or multiple calculation results in the vertical direction are used, and the column coordinates corresponding to the maximum value among the multiple calculation results are determined as the center column coordinates of the ROI.

[0009] In one implementation, before determining the column coordinates corresponding to the maximum value among multiple calculation results as the center column coordinates of the region of interest, the method further includes: splitting the multiple calculation results into several pipelined data levels; comparing each pipelined data level pairwise and recording the address corresponding to the larger value output by each level; and obtaining the maximum value among the multiple calculation results after multiple cycles of comparison.

[0010] In this implementation, the comparison logic of the projection calculation results is split into several pipeline clock stages for execution, which can quickly find the maximum value among multiple calculation results.

[0011] In one implementation, the proportion of spot pixels in each aiming scene image is applied to image frames within a single period.

[0012] In this implementation, the proportional reuse within a single cycle can be used for subsequent image frames, thus reducing computational overhead.

[0013] In one implementation, before determining the proportion of the number of light spot pixels in each aiming scene image, the method further includes: determining that shooting training parameters have changed.

[0014] In this training method, when shooting training parameters change, such as changing shooting training distance or scope magnification, the ratio will be recalculated to adaptively adjust the ratio.

[0015] In one implementation, during the acquisition of the next frame of image data, the region of interest in each aiming scene image is cropped according to the proportion of the number of light spot pixels in each aiming scene image.

[0016] In this implementation, the region of interest of the previous frame image is calculated during the data transmission of the next frame, which can reduce processing latency.

[0017] Secondly, an electronic device is provided, mounted behind the sight of a firing body, including functional modules for performing the method of the first aspect. For example, the electronic device includes a aiming point image processing unit and an aiming point trajectory measurement unit. The aiming point image processing unit is used to acquire at least one aiming scene image including the position of a light spot on a target. The aiming point trajectory measurement unit is used to, for each of the at least one aiming scene images, crop a region of interest in each aiming scene image according to the proportion of the number of light spot pixels in each aiming scene image, obtaining at least one region of interest, and determine the aiming point trajectory based on the at least one region of interest and the firing action of the firing body. For details, please refer to the detailed description in the method examples; further details are omitted here.

[0018] Optionally, the electronic device further includes a memory. The memory stores computer programs, instructions, or data. The processing unit is coupled to the memory and the transceiver unit. When the processing unit reads the computer program, instructions, or data, it causes the communication device to execute the method performed by the receiver in the above method embodiments.

[0019] Thirdly, this application provides a computer-readable storage medium storing a computer program or instructions that, when executed, implement the method described in the first aspect above.

[0020] Fourthly, a computer program product is provided, the computer program product comprising: computer program code or instructions, which, when the computer program code or instructions are executed, cause the method in the first aspect to be performed. Attached Figure Description

[0021] Figure 1 This is a schematic diagram illustrating an application scenario provided in the embodiments of this application; Figure 2 A schematic diagram of a sniper aiming point trajectory measurement system provided in an embodiment of this application; Figure 3 A flowchart illustrating the aiming point trajectory measurement method provided in this application embodiment; Figure 4 This is a schematic diagram illustrating the principle of the projection method; Figure 5 This is a schematic diagram of horizontal projection buffer accumulation provided in an embodiment of this application; Figure 6 This is a schematic diagram of vertical projection buffer accumulation provided in an embodiment of this application; Figure 7 This is a schematic diagram of image data caching provided in an embodiment of this application. Detailed Implementation

[0022] Simulated shooting training typically involves providing trainees with theoretical guidance beforehand, followed by target aiming to adjust shooting posture and aiming points. However, simulated shooting training cannot provide trainees with a realistic shooting experience, limiting its effectiveness. In contrast, live-fire shooting training offers trainees a genuine shooting experience, which helps improve training outcomes.

[0023] In live-fire training, evaluating shooting skills is crucial for improving training effectiveness. Accurate evaluation helps identify deficiencies in the shooter's training process, allowing for targeted improvements to training methods. Shooting skill is related to the trainee's shooting movements, and the aiming point trajectory reflects the trainee's mastery of these movements. For long-range sniping training, image processing is needed to determine the aiming point trajectory. Due to limitations in computing resources and environmental interference in outdoor shooting training, the real-time performance and accuracy of the aiming point trajectory still need further improvement.

[0024] Therefore, the solution provided in this application embodiment is as follows. In this application embodiment, the acceleration and angular velocity of the shooter are quantified into shooting evaluation indicators, such as stability, accuracy, and consistency. On the one hand, compared with processing and analyzing attitude trajectories, the computational load is smaller and the processing complexity is lower, which helps to improve evaluation efficiency. On the other hand, visually displaying at least one shooting evaluation indicator is more objective and helps to improve evaluation accuracy.

[0025] The method provided in the embodiments of this application can be applied to, for example, Figure 1 The sniping scenario or aiming point trajectory measurement system shown. For example... Figure 1 As shown, the shooter fires at the target 10 using the shooting body 20. The target 10 is equipped with an infrared emitter 101, which can be positioned at the center of the target. The infrared rays emitted by the emitter 101 have a wavelength of 850 nanometers. Since infrared light in the 850-nanometer wavelength band is invisible to the human eye, using an infrared emitter with a wavelength of 850 nanometers does not affect the shooter's aiming.

[0026] It is understandable that the key to driving the infrared transmitter 101 lies in high frequency, high current, and instantaneous protection. Because the infrared transmitter 101 has low withstand voltage and low internal resistance, the stability of the injected current directly affects its driving. The infrared transmitter 101 requires a drive circuit with low ripple and minimal glitches, maintaining a constant current. Therefore, this embodiment uses a constant current source system to drive the infrared transmitter 101. The overall design employs a deep negative feedback control principle to directly and effectively control the drive current, thus achieving the lowest current deviation and the highest output stability of the infrared transmitter 101.

[0027] The firing body 20 is equipped with a sight 201, and an aiming point image processing unit is located at the rear of the sight 201. (See also...) Figure 2 The aiming point image processing unit includes an optical path processing component and an image sensor. The image sensor can be a CMOS image sensor, with an infrared filter positioned on the side of the image sensor closest to the sight to eliminate ambient light interference. The optical path processing component can be two prisms arranged parallel to the eyepiece of the sight 201. It is understood that the sight 201 also includes necessary components such as an objective lens, eyepiece, elevation adjustment knob, calibration tube, and internal optical components (e.g., capable of forming a first and second focusing plane), such as... Figure 2 As shown. This part is not the focus of this application, therefore, it will not be described in detail here.

[0028] like Figure 2 As shown, the aiming scene observed by the shooter's eye is focused onto the image sensor through the eyepiece of the sight 201 and the optical path processing component, forming an image of the aiming scene. The optical path processing component performs beam splitting on the sight 201, and the image sensor digitally images the aiming scene, enabling long-distance aiming point trajectory measurement. Optionally, the optical path processing component can achieve 5:5 beam splitting, thus separating the aiming scene observed by the human eye into the imaging optical path. It is understandable that when the shooter adjusts the posture of the shooting body 20, while the target position remains unchanged, the target's position in the image sensor will change, thus reflecting the change in the aiming point trajectory.

[0029] Figure 3 An embodiment of this application provides a method for measuring aiming point trajectory, the flow of which is described below: Step S301: Acquire at least one aiming scene image including the position of the light spot on the target.

[0030] In this embodiment, the firing body 20 is further provided with one or more image sensors for acquiring aiming scene images of the position of the light spot on the target. Each time an image sensor acquires an aiming scene image, it transmits the acquired aiming scene image to the aiming trajectory measurement unit, so that the aiming trajectory measurement unit can obtain at least one aiming scene image. The aiming trajectory measurement unit can process at least one aiming scene image to obtain the aiming trajectory.

[0031] Step S302: For each aiming scene image in at least one aiming scene image, extract the region of interest in each aiming scene image according to the proportion of the number of light spot pixels in each aiming scene image, and obtain at least one region of interest.

[0032] Because the original aiming scene images are inevitably affected by external factors such as lighting during acquisition and generation, resulting in relatively poor image quality, the aiming trajectory measurement unit processes any acquired aiming scene image to extract the target spot center. Based on the target spot center extracted from multiple aiming scene images and the firing action of the firing device, the aiming trajectory is determined. After determining the aiming trajectory, the aiming trajectory measurement unit can store it.

[0033] Specifically, for any one of at least one aiming scene images, taking the first aiming scene image as an example, the aiming point trajectory measurement unit preprocesses the first aiming scene image. For example, it removes noise from the first aiming scene image to highlight the target's features, obtaining an image including the target's features. To reduce the influence of atmospheric turbulence, the background in the image including the target features is removed, that is, the target and background are segmented to obtain an image without background, reducing the amount of information. Then, a partial region of interest (ROI) is cropped from the background-removed image to obtain the region of interest, which can also be understood as the effective spot area, to reduce the computational load of image processing. Afterwards, morphological filtering algorithms can be used to remove burrs and small connected regions to obtain the effective spot area. Then, contour extraction is performed on the effective spot area to obtain the target contour. Subsequently, the target spot center is determined based on the target contour using a centroid algorithm.

[0034] Considering the significant difference between the aiming point pixel value and the background pixel value, the aiming point trajectory measurement unit can also convolve the first aiming scene image to extract the spot edge information, thereby determining the coordinate information of the spot edge based on the spot edge information. The coordinate information of the spot edge can characterize the approximate position of the aiming point in the first aiming scene image. During normal distance sniping, the number of pixels occupied by the aiming point is in the range of 100 to 2000. Combining the edge coordinate information of the aiming point, a region of interest (ROI) is cropped from the first aiming scene image with this coordinate as the center to obtain the effective spot area.

[0035] For example, the target scene image can be binarized, and the pixels of the binarized image can be projected to each row and column. Based on the large contrast difference between foreground and background pixels, image features can be extracted. Since the foreground pixel values ​​are larger and the background pixel values ​​are smaller, the maximum value of the projection result in the current frame can be recorded, and the row and column coordinates corresponding to the maximum value can be used as the center coordinates of the ROI region, thus completing the ROI region localization.

[0036] It can be understood that projection is essentially image feature statistics, mapping an input two-dimensional grayscale image into a one-dimensional projection sequence independent of the X and Y axes. The horizontal grayscale projection can be calculated using formula (1-1), and the vertical grayscale projection can be calculated using formula (1-2).

[0037] in, It is the grayscale projection value of the x-th row; It is the grayscale projection value of the y-th column, such as Figure 4 As shown, the shaded area represents the high pixel value area, the X-axis represents the projection result of each column, and the Y-axis represents the projection result of each row.

[0038] In traditional ROI, a fixed threshold (grayscale value) is used for ROI. On the one hand, in scenarios with complex lighting changes or dynamic adjustments in the shape of the trajectory spot, it is easy to miss the target or mis-extract the background. On the other hand, the use of a uniform threshold globally fails to take advantage of the pixel distribution characteristics of the trajectory spot, resulting in insufficient suppression of background noise. This not only increases the interference of subsequent centroid calculation, but also further restricts the real-time performance of the overall system.

[0039] For example, when using the fixed threshold projection method to extract ROI, if the threshold is not set properly, some brighter pixels in non-spot areas of the image will also participate in the accumulation of row and column pixels, affecting the calculation of ROI center coordinates. This will cause the extracted ROI window to not completely include the spot or only a part of the spot, thus affecting the subsequent centroid calculation. Furthermore, since the external environment will continue to change, the appropriate fixed threshold will also change accordingly. Therefore, the fixed threshold method has low robustness and is not suitable for outdoor shooting training scenarios.

[0040] Therefore, in this embodiment, a first threshold can be determined based on the proportion of spot pixels in the first aiming scene image. Then, a Region of Interest (ROI) is performed on the first cropped aiming scene image based on the first threshold to determine the region of interest in the first aiming scene image. Since the first threshold is dynamically determined based on the proportion of spot pixels, it can adaptively and accurately match the target features. Furthermore, for pixels higher than the first threshold, row and column accumulation operations are performed, which effectively suppresses background interference, preserves the core features of the trajectory, and significantly reduces redundant computation.

[0041] In this embodiment, considering that the aiming point spot appears as a bright circular area in the image with a significantly higher grayscale value than the background, facilitating threshold segmentation, grayscale threshold filtering is used instead of binarization. Only pixels in each row and column with a value greater than the first threshold need to be accumulated. For example, the center coordinates of the ROI can be calculated using formulas (1-3) and (1-4):

[0042] in, and These are the X and Y coordinates of the center of the ROI, respectively, and T is the first threshold.

[0043] The first threshold can be determined based on the proportion of spot pixels in the entire image. For example, a threshold selection function can be constructed based on the number of spot pixels in the first aiming scene image, and the first threshold can be determined based on this threshold selection function. For example, define... To determine the number of pixels in the light spot, sort the grayscale histogram from low to high according to the grayscale values ​​[0:255], and calculate the cumulative number of pixels using formula (1-5):

[0044] The first threshold T is when the cumulative pixels exceed... The largest value among all grayscale values ​​satisfies formula (1-6):

[0045] in, The value is calculated based on the specific shooting training scenario, for example, Satisfies formula (1-7):

[0046] in, d is the distance between the target and the scope; M is the magnification of the scope; f is the focal length of the lens; p is the pixel size of the CMOS sensor; R is the CMOS resolution; C is the radius of the light source at the center of the target.

[0047] Specifically, the aiming point trajectory measurement unit includes a threshold calculation module. This module classifies and statistically analyzes the image data based on its grayscale bit width *n*, classifying pixels by value to construct a full-image pixel grayscale histogram, thus quantifying the image's grayscale distribution characteristics. Then, the histogram data is successively accumulated in descending order of grayscale value to generate a cumulative grayscale pixel distribution sequence, reflecting the total number of pixels at different grayscale levels and above. Based on a pre-set threshold *N* for the number of light spot pixels, elements in the cumulative grayscale pixel distribution sequence greater than the number of light spot pixels are marked as 1, and the remaining elements are marked as 0, achieving binarization. Finally, by summing the binarized sequence, the position of the first element with a value of 1 is determined, and the grayscale value corresponding to this position is the final threshold *T*. The construction of the grayscale histogram can be completed after image output, and the accumulation of the grayscale histogram requires *n-1* clock cycles. Both the binarization and accumulation operations can be completed within one cycle.

[0048] Based on a first threshold, the region of interest (ROI) in the first aiming scene image can be extracted. For example, the first aiming scene image data can be projected horizontally and vertically, that is, the accumulated result of each row of pixels and the accumulated result of each column of pixels in the image can be calculated, and the maximum value of the output results can be compared to obtain the center coordinates of the ROI. It can be understood that in the image data stream processing architecture based on row scanning input, there is a significant difference in the hardware implementation complexity of horizontal and vertical projection. Image data is input sequentially from left to right and from top to bottom, row by row. Horizontal projection can be implemented through a pipelined accumulator, while vertical projection uses a buffer for accumulation. Assuming that the number of horizontal image pixels in a frame is n, such as... Figure 5 As shown, the buffer depth is set to n. During image data input, the pixel values ​​of each row are accumulated in real time, and the current row's accumulated result is dynamically updated with the historical maximum value (i.e., the first threshold) using a comparator. When a frame of image data has been input, the row coordinate corresponding to the maximum value output by the comparator is the horizontal center coordinate of the ROI.

[0049] like Figure 6 As shown, assume that the number of vertical pixels in a frame is m. The input image data is allocated, with each row of pixels being allocated and then sequentially added to the buffer bit by bit until the last pixel of the frame is reached, completing the statistical analysis of the vertical projection results. After projection, n calculation results are obtained in the vertical direction. The column coordinate corresponding to the maximum value among these n results is the column coordinate of the center of the ROI in the vertical direction.

[0050] To quickly find the coordinates corresponding to the maximum value among n values, this embodiment of the application, based on the design concept of parallel pipelined computation, splits the comparison logic of the projection calculation results into several pipelined clock stages. Each pipeline stage performs pairwise comparisons, recording the address corresponding to the larger value output by each stage. After multiple cycles of comparison, the address corresponding to the maximum value of the vertical statistical results can be obtained. Where n is not an integer power of 2, or when the calculation result is odd in subsequent processing, an irrelevant constant 0 is added to make the number of calculation results an integer power of 2.

[0051] The Region of Interest (ROI) (or effective spot area) is obtained and processed. For example, the target outline and target spot center are determined from the effective spot area. The trajectory formed by the target spot center determined in each aiming scene image is the motion trajectory of the aiming point. In this embodiment, the first aiming scene image is convolved to extract the spot edge information to estimate the position of the aiming point. This can be obtained simply by traversing the image, thus consuming less resources, saving computational resources, and improving the efficiency of estimating the aiming point trajectory.

[0052] Furthermore, in this embodiment, a strategy of "single-cycle threshold reuse + cross-cycle recalculation" is adopted to determine the first threshold. In other words, only the first frame is used to calculate the threshold, and subsequent frames are reused to reduce computational overhead. Or, the first threshold applies to image frames within a single cycle. When shooting training parameters change, such as changing shooting training distance or scope magnification, the first threshold is recalculated.

[0053] Furthermore, in the data caching and processing mechanism design, this application's embodiments employ a single-frame rolling caching strategy to achieve efficient data caching and reading / writing. For example... Figure 7 As shown, when the next frame of image data arrives, the ROI boundary coordinates of the previous frame are calculated. During the acquisition of the next frame of image data, the region of interest (ROI) in each target scene image is cropped according to a first threshold. In other words, while new data is written to the cache, the previous frame of data is simultaneously read out for ROI extraction. This is equivalent to triggering a ping-pong operation for caching. The extracted ROI region is directly stored and used for subsequent centroid calculation, ensuring that the centroid coordinates of the first frame of image data are calculated before the third frame of image data arrives.

[0054] For example, based on the image sensor configuration, the interval T2 between two 100Hz image frames is 10.1ms. The image data is input via a 16-channel dual-edge circuit with a clock frequency of 148.5MHz. Transmitting one frame of 2448*2048*8 image data requires 1,253,376 clock cycles, with T1 being 8.4ms. The data processing clock frequency is 200MHz, employing a pipelined computation method. When the nth frame of data arrives, the buffer stores the (n-1)th frame of data, while the on-chip storage of the ROI region is the (n-2)th frame of data. The entire process requires that the ROI coordinates of the (n-1)th frame of data be calculated and the centroid coordinates of the (n-2)th frame be calculated when the nth frame of data arrives.

[0055] Step S303: Determine the aiming trajectory based on at least one region of interest and the firing action of the shooting body.

[0056] Specifically, the aiming trajectory measurement unit can determine the aiming trajectory based on at least one aiming scene image combined with the firing action of the firing body. The firing body 20 is also equipped with an attitude measurement unit, which can be an inertial measurement unit (IMU) sensor. It should be noted that this embodiment does not limit the specific location of the attitude measurement unit on the firing body 20. For example, the attitude measurement unit can be located on the sight or other possible locations.

[0057] The attitude measurement unit can collect attitude parameters of the firing body, such as its attitude, velocity, and acceleration. The attitude data of the firing body can be used to determine its firing action and to eliminate interference from tilting, vibration, etc. The firing action of the firing body includes live-fire, dry-fire, or impact firing. It is understood that the spectral characteristics of the acceleration signals from live-fire, dry-fire, and impact firing are different. For example, the acceleration of live-fire is relatively large, and the firing action can be detected by the amplitude; the acceleration of dry-fire varies significantly, and detecting the firing action cannot rely solely on amplitude but also requires waveform analysis to distinguish between dry-fire and impact firing. Therefore, in this embodiment, the vibration signal when the firing pin impacts the primer is sensed by the accelerometer in the IMU, and by comprehensively utilizing the attitude of the firing body and the spectral characteristics of the vibration signal generated when the firing pin impacts the primer, the firing action can be detected and identified. Specifically, the attitude measurement unit transmits the collected attitude parameters to the aiming trajectory measurement unit. The aiming trajectory measurement unit determines the aiming trajectory based on at least one aiming scene image and the firing action determined based on attitude parameters.

[0058] Currently, shooting training relies heavily on bullet impact feedback, lacking monitoring and evaluation of the shooter's muscles and other bodily functions to aid training and improve effectiveness. Three factors typically influence shooting performance: grip stability, aiming, and trigger control. These three factors are interconnected and inseparable. Grip stability is fundamental to shooting; the quality of grip movement directly determines target practice results. A stable and consistent grip, consistent aiming at all three points, and uniform, vertical firing are essential for improving accuracy. Grip stability reflects the shooter's ability to control muscles and prevent unnecessary movements; aiming ability is the shooter's ability to point the gun at the bullseye; trigger control is the shooter's ability to time the firing and pull the trigger cleanly and decisively. During shooting, grip stability and aiming significantly impact the final result, directly affecting the aiming point trajectory. Therefore, real-time monitoring of the aiming point trajectory can be used for shooting ability assessment and further enhance shooting training levels.

[0059] Furthermore, the system provided in this application utilizes the spot displacement of the infrared emitter on the target to characterize the change in the aiming point during shooting. This avoids the aiming point trajectory being affected by environmental factors such as atmospheric turbulence, resulting in a more accurate aiming point trajectory and making it suitable for medium- and long-range shooting training. The attitude measurement unit can analyze the attitude of the shooting device, making it applicable to live-fire training. This allows the shooter using the shooting device to experience a realistic shooting experience and helps improve shooting training effectiveness by improving shooting posture and other aspects during the training process.

[0060] Currently, most simulation training equipment has a low degree of automation and cannot meet the requirement of real-time detection of the shooting body's motion parameters. Naturally, it is also impossible to adjust the shooter's shooting body's motion in a timely manner, provide targeted training, improve shooting posture, and thus gradually improve shooting training results and target shooting training efficiency.

[0061] Therefore, in this embodiment of the application, the anchor point trajectory measurement system may further include a terminal and a wireless transmission module. The wireless transmission module can transmit the data of the anchor point trajectory measurement module to the terminal. The terminal can analyze the acquired data to facilitate timely adjustment and targeted training of the shooter's actions at various stages of shooting.

[0062] For example, the terminal can acquire and process the aiming trajectory to obtain an aiming trajectory in at least one dimension. This at least one dimension includes a first dimension, where the aiming trajectory in the first dimension indicates the stability of the firing body during a period before and after the firing action; at least one dimension includes a second dimension, where the aiming trajectory in the second dimension indicates the translation of the aiming trajectory in the second dimension during the period before and after the firing action; and at least one dimension includes a third dimension, where the aiming trajectory in the third dimension indicates the translation of the aiming trajectory in the third dimension during the period before and after the firing action. For example, the terminal can display the aiming trajectory as an R(t) curve, x-curve, y-curve, trajectory diagram, etc., and can zoom in and out of the target image as needed for easy viewing and evaluation of the firing results. The R(t) curve characterizes the stability of the firing body during a period before and after firing; the x-curve and y-curve respectively characterize the translation of the aiming trajectory along the X-axis and Y-axis during the period before and after firing.

[0063] The terminal can record, display, and replay aiming trajectories in real time, using different colors to show the shooter's movement trajectory at different time points before and after firing. For example, the terminal uses a red line to show the muzzle jump instantaneously 0.2 seconds before firing; a yellow line to show the muzzle trajectory 1 second before firing, thus assessing the stability of the shooting stance; a light blue line to show the muzzle trajectory up to 1 second before firing; and a gray line to show the muzzle trajectory 0.2 seconds after firing. After the shooting session, the sniper's shooting skills can be evaluated based on four aspects: score, aiming, stability, and firing rate, in order to improve the sniper's skill level.

[0064] This application embodiment utilizes the spot displacement of an infrared emitter to represent changes in the shooter's aiming point. By analyzing data such as the sniper's shooting accuracy, rate of fire, and aiming point trajectory, the shooter can adjust the shooting action of the firing device in a timely manner during subsequent shooting training, improving shooting posture and thus gradually improving shooting training performance and target practice efficiency. Furthermore, this application embodiment is applicable to live-fire shooting, allowing shooters to fully experience necessary information such as recoil, wind direction and speed, and ballistic arc during training. Simultaneously, it helps them understand how to maintain shooting accuracy under the influence of detailed factors such as air humidity and wind direction on bullet trajectory and the optical effects of atmospheric turbulence in medium- and long-range shooting.

[0065] Steps S301-S303 can be implemented by programmable logic devices, such as field-programmable gate array (FPGA) chips, or by microcontrollers, or by other programmable logic devices. This application embodiment does not impose any limitations.

[0066] This application also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to execute the method in the above method example. For details, please refer to the detailed description in the method example, which will not be repeated here.

[0067] The embodiments provided in this application describe the methods from the perspective of the interaction between various functional modules that may be included in the aiming trajectory measurement system. To implement the functions of the methods provided in the embodiments of this application, the aiming trajectory measurement system may include hardware structures and / or software modules, implementing the above functions in the form of hardware structures, software modules, or a combination of hardware structures and software modules. Whether a particular function is executed in the form of hardware structures, software modules, or a combination of hardware structures and software modules depends on the specific application and design constraints of the technical solution.

[0068] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] Furthermore, the functional units in the embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0070] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for measuring the aiming point trajectory of a sight, characterized in that, Applied to an electronic device mounted behind a sight of a firing body, the method includes: Acquire at least one image of the aiming scene, including the position of the light spot on the target; For each of the at least one aiming scene images, a region of interest is extracted from each aiming scene image according to the proportion of the number of light spot pixels in each aiming scene image, thereby obtaining at least one region of interest; The aiming trajectory is determined based on the at least one region of interest and the firing action of the shooting body.

2. The method as described in claim 1, characterized in that, The method further includes: For each target scene image, sort the grayscale histograms corresponding to the grayscale values ​​from low to high, and calculate the cumulative pixel values. The largest value among all grayscale values ​​whose cumulative pixel value exceeds the number of spot pixels is determined as the proportion of the number of spot pixels in each aiming scene image.

3. The method as described in claim 2, characterized in that, The region of interest (ROI) in each aiming scene image is extracted based on the proportion of the number of light spot pixels in each image, including: During the image data input process for each target scene image, the pixel values ​​of each row are accumulated in real time, and the current row accumulation result is compared with the ratio by a comparator. The row coordinates corresponding to the maximum value output by the comparator are determined as the center row coordinates of the region of interest. The image data is allocated, and after a row of pixels is allocated, it is accumulated bit by bit into the buffer until the last pixel of a frame image ends, or multiple calculation results in the vertical direction. The column coordinates corresponding to the maximum value among the multiple calculation results are determined as the center column coordinates of the region of interest.

4. The method as described in claim 3, characterized in that, Before determining the column coordinates corresponding to the maximum value among the multiple calculation results as the center column coordinates of the region of interest, the method further includes: The multiple calculation results are split into several pipeline-level data; Each level of pipeline data is compared pairwise, and the address corresponding to the larger value of the comparison output at each level is recorded. The maximum value among the multiple calculation results is obtained after comparing multiple cycles.

5. The method as described in claims 2-4, characterized in that, The proportion of the number of light spot pixels in each aiming scene image applies to image frames within a single cycle.

6. The method as described in claim 5, characterized in that, Before determining the proportion of the number of light spot pixels in each aiming scene image, the method further includes: It was determined that the shooting training parameters had changed.

7. The method according to any one of claims 1 to 4, characterized in that, During the acquisition of the next frame of image data, the region of interest in each aiming scene image is extracted according to the proportion of the number of light spot pixels in each aiming scene image.

8. A computer-readable storage medium, characterized in that, Used to store computer programs, when the computer program When run on a computer, the computer performs the method as described in any one of claims 1 to 7.

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