Systems and methods for interactive, video-based, firearms training

Abstract

A system is configured for interactive, video-based, live-fire and / or dry-fire firearms training by projecting a target image onto a target screen, capturing video images of the target screen, synchronizing locations on the target screen with the target projected onto the target screen, detecting impacts on the target screen and correlating the location of the impacts on the target screen with locations in the projected target. The system may update the target images over time, in response to detected hits, or in response to a timer. A video image may be used as a target image to simulate moving targets or other advanced scenarios to allow a shooter to practice shooting at moving targets even while using a live-fire firearm.

Description

SYSTEMS AND METHODS FOR INTERACTIVE, VIDEO-BASED, FIREARMS TRAININGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This Application claims priority under 35 U.S.C. § 119(e) to US Provisional Patent Application having Serial Number 63 / 686,709, filed on August 23, 2024, entitled Systems and Methods for Interactive, Video-Based, Live-Fire Firearms Training, the entire disclosure of which is incorporated herein by reference.BACKGROUND

[0002] Target shooting is enjoyed by millions of people a year and according to many reports, the number of people who routinely target shoot has increased over the past ten years and is continuing to increase. In the US alone, it is estimated that over 52 million people routinely shoot at targets. There are many different types of recreational shooting activities, from simple plinking with handguns or rifles at paper or steel targets, to skilled long-range rifle shooting matches that require a high degree of discipline and skill, to fun and fast-paced shooting of pistols at popup or stationary targets or shotgun shooting at skeet, trap, sporting clays and more. Apart from recreational shooting, there is a growing number of target shooters that practice as part of their occupation, such as law enforcement, military, and security personnel.

[0003] The growing number of people at shooting ranges continues to increase and these participants, whether there for sport, recreation, personal defense, or public defense, have a desire to improve their skills. However, at a busy shooting range that allows paper targets, it can be disrupting to require the range to go cold in order to move downrange to set up, inspect, score, or replace the targets. Similarly, at any of a number of shooting competitions, the range must go cold before competitors are allowed to move downrange to inspect, score, and replace their targets.

[0004] Moreover, in certain industries, a shooter must pass qualification tests. While these tests are designed to measure the skill of the marksman, they require some shots to be taken within a short time frame, and there may be some subjectivity in scoring the hit and determining whether it was taken within the allotted time.

[0005] Finally, current systems that allow training at moving targets often utilize simple mechanical motions, such as a paper target attached to a trolly riding on a zip line, or a target affixed to an oscillating arm. In either case, the moving target is quite predictable and offers little to no variation in the target movement. More recently dry-fire “virtual range” systems provide a more dynamic, interactive training environment, but require use of non-standard, laser-based firearms, which have a different weight, recoil, and operating systems than real duty weapons.

[0006] It would be advantageous if a system were capable of automatically scoring a shooting target, provide interactivity, and provide for an array of moving targets while allowing the shooter to utilize her own weapon. This would provide increased efficiency in practice or competition, among other things. These and other benefits will become readily apparent from the disclosure that follows.SUMMARY

[0007] The disclosed systems and methods provide for a system that allows live-fire or dry -fire training by incorporating a target animation or video projected onto a ballistic target screen. Long wavelength light (e.g., infrared “IR” or long wave infrared “LWIR”) or short wavelength light (e g., near-infrared “NIR”, or short wave infrared “SWIR”) is simultaneously recorded from the screen. In embodiments configured for live-fire, in which a bullet impacts the screen, a shooter directs and discharges a firearm at the projected targets displayed on the ballistic screen. Bullet impacts on the ballistic screen penetrate through the screen and leave thermal signatures which persist for anywhere from a few seconds to several minutes, depending on the screen material used. Impacts are detected in the infrared video in real time. Detected impacts may then be scored, or may be used to affect the projection in subsequent frames, such as accumulating drill scoring totals or game play. As used herein, the term “live-fire” is a broad term and relates to using live ammunition that allows a shooter to use a personal, or issued, firearm with live ammunition for training purposes. As used herein, the term “dry-fire” is a broad term and relates to using a firearm that does not discharge a bullet, but rather, may issue a beam of light from the firearm toward a target. The beam of light may be a laser beam that can be detected according to the systems and methods described herein.

[0008] According to some embodiments, a live-fire firearms training system includes a ballistic target screen comprising a polymer material configured to generate thermal signatures upon bullet impact, which may be a self-healing material; a projector configured to display moving target images onto the ballistic target screen; a thermal imaging camera configured to capture thermal images of the ballistic target screen; one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to project a sequence of target images onto the ballistic target screen; capture thermal images of the ballistic target screen during live-fire shooting; detect thermal signatures of bullet impacts in the captured thermal images; temporally synchronize the projected target images with the captured thermal images; spatially synchronize locations in the projected target images with corresponding locations in the captured thermal images; determine whether detected bullet impacts coincide with projected targets based on the spatial and temporal synchronization; and modify subsequent projected target images based on the determined bullet impacts.

[0009] In some cases, the instructions further cause the system to identify multiple simultaneous shooters based on at least one of temporal patterns of impacts, spatial clustering of impacts, thermal signature characteristics, or acoustical signatures; and maintain separate scoring for each identified shooter. Identifying multiple simultaneous shooters may include the steps of defining virtual shooting lanes on the ballistic target screen; associating impacts with specific lanes based on impact location; and tracking performance metrics separately for each lane.

[0010] In some examples, detecting thermal signatures includes computing an exponentially weighted moving average (EWMA) of prior thermal frames; subtracting the EWMA from a current thermal frame to generate a difference image; applying adaptive thresholding to the difference image based on measured background noise; and identifying contours in the thresholded image that satisfy predetermined validation criteria. The predetermined validation criteria may include a contour area between 10 and 500 pixels; a contour circularity greater than 0.6; temporal persistence across at least 2 consecutive frames; and spatial stability with movement less than 5 pixels between frames.

[0011] In some cases, the self-healing polymer material is one or more of embedded microcapsules containing a healing agent and a polymer matrix that reforms chemical bonds after bullet passage.

[0012] In some examples, temporally synchronizing includes timestamping each projected frame and each captured thermal frame; measuring system latency during calibration; and applying lag compensation based on the measured latency.

[0013] In some examples, spatially synchronizing includes projecting a calibration pattern onto the ballistic target screen; identifying corresponding points in the projection space and thermal image space using a thermal emitter, which may be a projector bulb, computing a homography matrix based on the corresponding points; and applying the homography matrix to map impact locations between coordinate spaces.

[0014] In some instances, modifying subsequent projected target images comprises at least one of changing target size based on shooting accuracy; adjusting target movement speed based on hit rate; splitting targets into multiple smaller targets upon impact; or introducing new targets based on performance metrics.

[0015] According to some embodiments, a method for interactive live-fire firearms training, includes displaying a video sequence comprising moving targets onto a ballistic screen using a projector; continuously capturing thermal video of the ballistic screen during live-fire shooting using a thermal imaging camera; computing difference images between sequential thermal video frames to identify pixels exhibiting increasing thermal intensity indicative of bullet impacts; applying threshold filtering to the difference images to generate binary images; detecting contours in the binary images corresponding to bullet impacts; validating detected contours based on area, circularity, and temporal persistence; mapping validated impact locations from thermal image coordinates to projected image coordinates using a pre-calibrated homography matrix; and scoring impacts based on correspondence with target locations in synchronized projected frames.

[0016] In some instances, the method further includes the step of dynamically adjusting target difficulty based on scoring results.

[0017] The method may further include registering multiple shooters before a training session; assigning each shooter to a designated region of the ballistic screen; associatingdetected impacts with specific shooters based on impact location; and providing individualized feedback to each shooter.

[0018] In some examples, computing difference images comprises maintaining an exponentially weighted moving average (EWMA) of thermal frame intensities; updating the EWMA with an adaptive weighting factor based on scene dynamics; and computing pixelwise differences between current frames and the EWMA.

[0019] The method may include detecting environmental conditions including ambient temperature and lighting; adjusting detection thresholds based on the detected environmental conditions; and modifying projector settings to maintain target visibility.

[0020] In some cases, validating detected contours comprises filtering contours based on geometric properties; tracking contours across multiple frames to verify persistence; rejecting contours that exhibit movement exceeding a threshold distance; and confirming impact authenticity using acoustic sensors as secondary validation. The contours can be further filtered as random contours, and those contours the system identifies as potential impacts.

[0021] The method may further include one or more of storing impact data including location, time, and shooter identification; analyzing shooting patterns to identify training needs; generating performance reports with improvement recommendations; and comparing performance across multiple training sessions.

[0022] According to some embodiments, a computer-readable medium storing instructions that, when executed by one or more processors, cause a live-fire training system to calibrate spatial correspondence between a projection system and a thermal imaging system using a homography estimation; project interactive targets onto a ballistic screen; detect bullet impacts on the ballistic screen using thermal imaging; correlate impact locations with projected target positions in real-time; update target behavior based on detected impacts; and track and store performance metrics.

[0023] The instructions may further cause the system to implement safety protocols including automatic cessation upon detecting impacts outside designated areas; monitor shooter positions to ensure safe firing angles; and interface with range safety systems for emergency shutdown.

[0024] According to some embodiments, a ballistic target screen for live-fire training includes a front layer comprising self-healing polymer material between 3 and 25 millimetersthick; and a backing layer providing structural support to the front layer; wherein the self- healing polymer material generates thermal signatures persisting between 2 and 300 seconds upon bullet impact.

[0025] In the ballistic target screen the polymer material may comprise one or more of polyurethane or polyurea compounds optimized for thermal signature generation and rapid heat dissipation.

[0026] According to some embodiments, a system for firearms training includes a projector; an image capture device; a target screen; and a computing device operably coupled to the projector and image capture device and configured with instructions that, when executed, cause the computing device to: display an image onto the target screen; detect, using the image capture device, an impact on the target screen; and synchronize a location of the impact on the target screen with a location in the image displayed on the target screen. As will be described in detail hereinafter, the impact on the target screen may be a bullet, such as in the case of the system operating in a live-fire training mode, or the impact may be a laser impact when the system is operating in a dry-fire training mode.

[0027] In some cases, the image capture device is a thermal imaging camera configured to capture a thermal image of the target screen.

[0028] According to some embodiments, a method for video-based, live-fire firearms training, includes the steps of displaying a target image onto a target screen to generate a projected target; capturing images of the target screen and projected target; temporally synchronizing the projected target and the captured images; spatially synchronizing the projected target and the captured images; detecting bullet impacts on the target screen; and scoring the bullet impacts by correlating a location of the bullet impacts on the target screen with a location of the projected target at a time of the bullet impact.

[0029] The system may be configured to update the projected target image based upon a detected impact, such as making the target harder or easier to hit, make a target appear or disappear after a certain time, display moving targets, among others.

[0030] A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operationsor actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings are part of the disclosure and are incorporated into the present specification. The drawings illustrate examples of embodiments of the disclosure and, in conjunction with the description and claims, serve to explain, at least in part, various principles, features, or aspects of the disclosure. Certain embodiments of the disclosure are described more fully below with reference to the accompanying drawings. However, various aspects of the disclosure may be implemented in many different forms and should not be construed as being limited to the implementations set forth herein. Like numbers refer to like, but not necessarily the same or identical, elements throughout.

[0032] FIG. 1A illustrates a system configured for interactive, video-based, firearms training, in accordance with some embodiments;

[0033] FIG. IB illustrates a cross-sectional representation of a target screen, in accordance with some embodiments;

[0034] FIG. 2 illustrates a sample process flow for projecting a video target and detecting shots on target, in accordance with some embodiments;

[0035] FIG. 3 illustrates a sample process flow for contour-based impact detection, in accordance with some embodiments;

[0036] FIG. 4 illustrates a sample system, showing the impact screen with projected moving targets, in accordance with some embodiments;

[0037] FIG. 5 illustrates a system for interactive, video-based, firearms training, in accordance with some embodiments;

[0038] FIG. 6 illustrates a sample process flow for temporal synchronization, in accordance with some embodiments; and

[0039] FIG. 7 illustrates spatial synchronization for use with the systems described herein, in accordance with some embodiments.

[0040] Figure 8 illustrates a biometric integration and stress monitoring system for correlating physiological parameters with shooting performance, in accordance with some embodiments.DETAILED DESCRIPTION

[0041] According to some embodiments, a system is described that can project a target, including a moving target, onto a shooting screen, know the location of the projected target on the screen, detect bullet impacts to the screen, and spatially coordinate the impact onto the projected target. In some cases, the system may be stored and executed on consumer-grade mobile computing devices (e.g. an iPhone, a tablet, a telephone, a video camera, a laptop, a smartphone, or otherwise). Of course, some installations may be more permanent, and the computing resources may be provided by a desktop computer, a cloud-based server, or some other type of computing devices, including a mobile computing device or gaming device. In some cases, the system includes a video camera device that is pointed at the target of interest, and the system is configured to identify the bounds of the target, classify the target, determine shot impacts on the target, and score the impacts on the target. In some cases, the system is configured to prompt a shooter as to the shooting stage. For instance, the system may be configured for utilization during a qualification test, such as a law enforcement pistol qualification test, and the system may prompt a user that the present stage requires sending three shots downrange after drawing from a holster within five seconds. In some cases, the system is aware of how many shots to expect during a shooting stage (referred to as a string of fire), and may prompt a user with information associated with a current shooting stage, such as number of shots, timeframe, shooting position, starting position, or a required movement during the shooting string. In some cases, a shooter may enter information associated with a shooting stage, such as, for example, a number of shots the system should expect, the firearm used, and the distance to the target, among others. In some cases, the system is manually started and stopped and only identifies shots on target during a time at which the system has been started.

[0042] In some cases, the described system operates in near real-time on a single, consumer recording device such as a mobile phone using only a modest amount of training data. As used herein, the terms “real-time” or “near real time” are broad terms and in the context of this disclosure, relate to receiving input data, processing the input data, and outputting the results of the data analysis with little to no perceived latency by a human. In other words, a system as described herein that outputs analyzed data within less than onesecond is considered near-real time. Put another way, “real-time” is that the delay between input and output, or stimulus and response, is less than a prescribed bound, such as one second, or 0.5 seconds, or 0.2 seconds, or 0.1 seconds, or 0.05 seconds, or some other bound. A system that operates in real-time or near-real time may limit the amount of computation for machine-learning, or at least training models, that the method can use to characterize a particular target acquisition, classification, and scoring. The system may use other types of hardware, including a video camera, a thermal imaging camera, a projector, a microphone, a ballistic impact screen, among others. In some cases, the components are integral with a mobile computing device, or may be attached to a mobile computing device. For instance, a projector may be attached to a mobile computing device and used to project an image on the screen. Similarly, the system may use a camera integral with the mobile computing device, or may use an external camera, which may include a video camera, a thermal imaging camera, or a combination, or otherwise. The camera may be coupled to the mobile computing device.

[0043] Referring to FIG. 1 A, a comprehensive firearms training system 100 includes multiple computing resources 102 that may be implemented in various configurations. The computing resources 102 may include cloud-based computing instances 102(1), 102(2) through 102(n), enabling distributed processing and multi-lane support. Each computing instance may handle specific processing tasks, such as impact detection, scoring, or video rendering, allowing for scalable performance across multiple simultaneous shooters. While multiple computing resources should be used, it will be appreciated that a single computing resource can be used to implement the systems and methods described herein, which may be a laptop, a desktop, a tablet, or some other type of computing architecture.

[0044] The processor(s) 104 may include specialized hardware for real-time image processing, such as graphics processing units (GPUs) optimized for computer vision tasks, field-programmable gate arrays (FPGAs) for low-latency impact detection, or tensor processing units (TPUs) for machine learning-based target recognition. The processor(s) 104 execute instructions stored in memory 106, which may include volatile memory for real-time processing buffers and / or non-volatile storage for persistent data.

[0045] The memory 106 stores multiple specialized modules 108, which may include one or more of an impact detection module 118, a coordinate transformation module 120, ascoring engine module 122, a video generation module 124, a synchronization module 126, a calibration module 128, a user interface module 130, a data analytics module 132, and a safety monitoring module 134. Each module 108 performs specific functions that collectively enable the real-time, interactive training experience.

[0046] A projector 110 is positioned relative to the target screen 112 using an alignment mechanism 136 that may include motorized pan-tilt heads, laser alignment guides, or fixed mounting brackets with precision adjustment screws. In some cases, the projector 110 includes a brightness of 3000 lumens or greater for indoor use or up to 6000 lumens or greater for outdoor applications, a native resolution of 1920x 1080 pixels or greater, and a refresh rate of 30-60-120 Hz to enable smooth motion of projected targets. The projector 110 may include a lens system 138 with adjustable zoom and focus to accommodate various distances to the target screen 112, typically ranging from 3 to 10 meters. It should be noted that additional distances are contemplated and, in fact, range distances have been used out to 300 meters from the firing line to the target screen 112.

[0047] The target screen 112, shown with concentric ring patterns in FIG. 1 A, and shown in cross section in FIG. IB, comprises a multi-layer ballistic material system 140. A front layer 142 consists of self-healing polymer material, such as polyurethane or polyurea compounds with a thickness of l-12mm, that provide a thermal signature generation when impacted. Any suitable ballistic material may be used, and in some cases, a self-healing rubber or polymer material or coating may be used. The polymer material may include embedded microcapsules containing healing agents that activate upon bullet impact. A backing layer 146 provides structural support and may comprise materials such as oriented strand board (OSB), plywood, coroplast, corrugated plastic, honeycomb, foam core, composite panels, or specialized ballistic backing materials. The screen 112 includes a frame system to support the screen down range.

[0048] The imaging sensor / camera 114 may be an infrared camera and may have a thermal sensitivity (NETD) of less than 50mK for optimal impact detection in any environment, resolution of 640x480 pixels or greater for adequate spatial discrimination, and frame rates between 9-60 Hz and may be synchronized with the projector refresh rate. In some embodiments, the camera 114 includes an infrared-transparent lens 152 made from germanium or chalcogenide glass materials with anti -reflective coatings optimized for the 8-14 pm wavelength range. In some cases, the camera 114 can be a variety of different cameras depending on the application. For example, the camera 114 may be configured to detect light in the long-wave infrared (e.g., thermal), or may be configured to detect signatures in the short-wave infrared spectrum. In some examples, the system may be configured as a dry-fire system. For instance, a firearm may activate a laser onto the target screen, and the camera 114 may be configured to detect the laser impact and will be described in later detail.

[0049] The computing resources 102, which may be a mobile computing device associated with a participant at a shooting range and may include any one or more of a number of mobile computing devices, such as, for example, a smart phone, a tablet computer, a lap top computer, a desktop computer, a video game console, or other suitable computing device. The computing resources 102 may further include data storage, which may be remote storage, such as a remote server or a cloud-based storage system, or local storage, or a combination. The data storage may store data on previous engagements (DOPE) which can allow for data tracking over time as well as comparative data between different shooters, different firearms, different ammunition, different targets, different environments, and the like.

[0050] The storage system may further allow historical trend analysis, which can be used to show shooter performance over time, including tracking improvements, successful training courses, evaluations, and qualifications. The data storage may also be analyzed to provide performance predictions, rankings, social features, among other benefits.

[0051] The system may incorporate one or more projectors 110 pointed at a target screen 112 and one or more imaging sensors 114 aimed at the screen, such as any suitable video camera or thermal imaging camera (hereinafter “camera”). In some cases, the imaging sensor 114 may be associated with the computing resources 102. For instance, in some embodiments, the computing resources 102 may be a smart phone with built in camera 114 or may include an externally attached camera 114.

[0052] The camera 1 14 may be pointed to capture images (e.g., video images) of the target screen 112. The target screen 112 may be located at any distance from the shooter and the camera 114 may be aimed and / or zoomed to capture images of a target projected onto the targe screen 112 by the projector 110.

[0053] The computing resources 102 may include instructions (e.g., modules 108) that allow the computing device to display, through the projector 110, a target onto the target screen 112, detect impacts on the target screen 112, such as through images captured by the imaging sensor 114 and score the impacts on the target screen 112, among other things.

[0054] As described above, the target screen 112 may include a ballistic screen material, which may comprise a self-healing material.

[0055] In general, self-healing mats are designed to endure and recover from damage, making them highly durable and reliable for various applications, including ballistic protection. The materials used in these mats typically combine traditional high-strength materials with advanced polymers capable of self-repair. Some examples may include elastomers or polymers that provide flexibility and resilience. Common examples include polyurethane, polyurea, and polyvinyl butyral (PVB). These materials are chosen for their ability to deform and then return to their original shape, an important property for absorbing impacts. In some cases, self-healing mats may incorporate composite layers that combine elastomers with fibers such as aramid (e.g., Kevlar®) or ultra-high-molecular-weight polyethylene (UHMWPE). These fibers enhance the mechanical strength and energy absorption capabilities of the mats.

[0056] Some self-healing mats may use nanomaterials. For example, materials like carbon nanotubes (CNTs) and graphene may be integrated into a polymer matrix. These nanomaterials improve the mechanical properties, such as tensile strength and stiffness, and may also contribute to the self-healing process.

[0057] Self-healing mats often utilize polymers with dynamic covalent bonds, which can break and reform in response to external stimuli like heat or pressure. This allows the material to "heal" after damage by re-establishing its molecular structure. Some self-healing mats rely on non-covalent interactions such as hydrogen bonding, ionic interactions, or van der Waals forces. These interactions are reversible and allow the polymer network to selfrepair without requiring external energy input. For clarity, several examples of self-healing mats used in the context of the described embodiments do not completely reclose after a penetration, but rather, the impacts may close slightly smaller than the penetration caused by a bullet, and remain as penetrations through the self-healing mat. The thermal signature caused by the penetration of the bullet will fade over time, which may be a few seconds to aminute or more, and thus, the thermal images of the ballistic screen appear to “self-heal” over time. In this way, a subsequent bullet that strikes in nearly the same space as a prior bullet will cause a new thermal signature in that location, and, to the thermal imaging camera, will appear as a new impact. This allows a ballistic screen to be used for hundreds, or thousands of rounds, and continue to result in accurate impact detection.

[0058] The self-healing capability of these mats is derived from their ability to autonomously repair damage through one of several mechanisms, including microcapsulebased healing, intrinsic self-healing polymers, and reversible polymer networks.

[0059] Microcapsules containing a healing agent (e.g., a polymer precursor or monomer) may be dispersed throughout the mat. When the mat is punctured or damaged, these microcapsules rupture, releasing the healing agent into the damaged area. The agent then undergoes polymerization or cross-linking, effectively sealing the breach and restoring the mat's integrity. This mechanism can localize the healing process to the damaged area, minimizing material waste and maintaining the mat's overall structural integrity.

[0060] Intrinsic Self-Healing Polymers have dynamic bonds within their molecular structure that can break and reform in response to damage. When the mat is damaged, the bonds at the damaged site break, but due to the dynamic nature of the bonds, they can reconnect over time, healing the material. This process can be triggered by external stimuli such as heat, light, or a chemical catalyst, depending on the specific polymer used. Intrinsic self-healing polymers can undergo multiple healing cycles, providing long-term durability and reducing the need for replacement or repair.

[0061] Reversible Polymer Networks are formed by polymers that can reversibly crosslink. When damage occurs, the cross-links are disrupted, but they can reform when exposed to certain conditions (e.g., elevated temperatures). This allows the material to regain its original properties after healing. Reversible polymer networks are particularly effective in maintaining the mechanical properties of the mat after healing, ensuring that the mat continues to perform as expected.

[0062] Self-healing mats are designed to effectively absorb and dissipate the kinetic energy from impacts. The elastomeric base materials allow the mats to deform under pressure, distributing the force of the impact across a wider area, which reduces the likelihood of penetration. In many cases, the force of the impact and the materialsdeformation in response releases energy in the form of heat. Thus, the mat exhibits elevated temperature at the localized point of impact, which can easily be seen by a thermal imaging device, such as the camera 114.

[0063] One of the interesting advantages of self-healing mats is their ability to recover from multiple impacts. Traditional target materials can suffer from cumulative damage, reducing their effectiveness over time. However, self-healing mats can restore some of their structural integrity between hits, and can quickly dissipate heat, thus extending their operational lifespan and allowing the systems described herein to differentiate between more recent and older impacts. In some cases, the target screen may include a ballistic rubber facing material supported by a rigid backing board. The facing material (i.e., the material closest to the shooter) may be chosen to provide good thermal characteristics, such as to clearly show impacts in the thermal video and quickly dissipate heat to avoid thermal saturation. The backing board, in those embodiments in which one is used, may be used to provide rigidity and may be selected to allow bullets to pass through cleanly without blowing out material at the bullet exit sites. Such suitable backing material may include wooden materials, such as plywood, oriented strand board (OSB), fiberboard, gypsum board, coroplast, or other suitable materials. The screen may be supported by a stand and may be supported on the ground, suspended from above, or deployable from a wall, for example.

[0064] In use, the projector 110 displays a drill, target animation, or video scenario on the screen. Simultaneously, the camera 114 captures video in a separate thread or process at a framerate that allows difference images to be determined. In some cases, the camera 114 is a thermal imaging camera that is able to detect thermal energy emanating from specific points on the screen after a bullet passes therethrough.

[0065] Unlike conventional cameras that create pictures using visible light, thermal cameras operate in the long- wavelength infrared spectrum, which extends up to 14,000 nanometers. A thermal camera works by detecting the heat emitted by objects and converting it into an electronic signal.

[0066] Thermal imaging cameras operate by detecting infrared radiation emitted by objects and converting it into visible images. Cooled thermal imaging cameras use sensors that operate at cryogenic temperatures. The cooling reduces thermal noise, allowing the camera to detect minute differences in infrared radiation. These cameras typically use photondetectors, such as mercury cadmium telluride (MCT) or indium antimonide (InSb), which are sensitive to mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands.

[0067] In contrast, uncooled cameras use microbolometers or thermopiles as their detection mechanism. These sensors detect infrared radiation by measuring changes in material resistance (microbolometers) or generated voltage (thermopiles) due to absorbed thermal energy. Uncooled sensors operate at ambient temperature, making them more practical for many applications despite being less sensitive than cooled sensors.

[0068] Lenses in thermal cameras are made from materials like germanium, chalcogenide glass, or zinc selenide, which are transparent to infrared radiation. These lenses focus infrared radiation onto the sensor. The lens design impacts the field of view (FOV) and the effective range of the camera. The electrical signals generated by the sensor (due to detected infrared radiation) are converted into digital signals. The image processing unit (IPU) processes these signals to create a thermal image. This involves tasks such as non-uniformity correction (NUC), noise reduction, and contrast enhancement. Thermal images are typically displayed using color palettes, where different colors represent different temperature ranges. This process, known as pseudo-coloring, helps users interpret thermal data more easily.

[0069] The system may use pseudo-coloring to discriminate more recent impacts from less recent impacts. In some cases, thermal cameras offer resolutions, such as 640x480 pixels or higher. This allows for detailed imaging and discrimination from impact to impact. A more standard resolution for thermal cameras is in the range of from about 160x120 to 640x480 pixels.

[0070] Some thermal cameras have a low Noise Equivalent Temperature Difference (NETD), often below 20 mK, meaning they can detect very small temperature variations. However, for most implementations described herein, higher NETD values are acceptable, typically between 50-100 mK. In use, it has been shown that a recent projectile impact on the ballistic screens described herein will show a white-hot impact on the camera for several seconds after the impact, which provide more than enough time for the impact to be detected and registered with the system.

[0071] In some cases, thermal cameras can be made to offer refresh rates up to 120 Hz or higher, providing smooth, real-time thermal imaging. However, in many cases, this isn’t necessary as most impacts on the screen from a single shooter will be separated in time bytenths of a second rather than hundredths of a second. Therefore, suitable refresh rates of thermal cameras may be on the order of between about 9 Hz and 60 Hz.

[0072] In some cases, a multi-spectral imaging camera may be incorporated into embodiments described herein. Thermal imaging cameras may be integrated with other imaging modalities, such as visible light cameras or LiDAR systems. This combination provides a more comprehensive view, and allows the system to cross check the hits / misses and new impacts vs old impacts.

[0073] According to some embodiments, in practice, a shooter 116 aims at the target screen 112 and the computing resources 102, by using the projector 110, projects a target image at the target screen 112. The camera 114 captures images of the screen and the system, by using sequential images captured from the camera 114, determines when the shooter fires a round that impacts the target screen 112. The system 100 then determines whether the impact on the screen hit a projected target and may score the hit, or take some other action based on the hit or on the miss. In some cases, the system 100 uses a timer to limit the time during which the shooter 116 can shoot at the targets projected onto the target screen 112.

[0074] In some cases, the target projected onto the screen may move about the screen, may appear or disappear, may become larger or smaller, or may pose a perceived threat to the shooter 116. For example, the images projected onto the screen may show a scenario in which a bad actor may raise and / or point a gun at the shooter and the shooter has a specified time to neutralize the threat.

[0075] In some cases, some components of the system, such as the computing device, projector, and camera reside in a single housing. In some cases, the housing may have wheels and a handle for easy transport to and from a shooting location. A power input to the housing may supply necessary power for the computing device, projector, and camera. The housing may have apertures to allow the lenses of the projector and camera to view the shooting screen through the housing, and the housing may also have cooling vanes and optionally one or more cooling fans to allow cooling air to circulate within the housing. The system may further include communication interfaces, which may be wired or wireless. The communication interface may allow the computing device to send a user interface remotely, such as to a smartphone, tablet, laptop, display screen, or other type of device that allows auser to receive information and / or interact with the system. For instance, a user may select specific drills to perform by using a touch screen on a displayed user interface, and may further provide voice commands to the system, such as an audible response to a “Ready?” prompt from the system. The user may repeat the word “ready” or say “yes” to inform the system that the shooter is ready for the drill to begin.

[0076] The system 100 supports multiple simultaneous shooters 116 through several mechanisms. In a lane-based configuration, each shooter 116 is assigned a designated shooting lane with a corresponding region on the target screen 112. The system tracks impacts within each lane and maintains separate scoring for each shooter 116. Lane boundaries may be physically marked on the floor, displayed on the target screen 112, or virtually defined within the system software.

[0077] For shooter identification, the system can employ multiple techniques. Temporal separation analysis can be used to identify different shooters based on the timing patterns of their shots, as different shooters exhibit characteristic rhythms and intervals between shots. Spatial clustering analysis can be used to group impacts based on their location patterns, as individual shooters tend to have consistent point-of-aim and shot groupings. Thermal signature analysis can be used to differentiate between shooters using different ammunition types or firearms, as different combinations produce slightly different thermal signatures in terms of intensity and decay rate, finally, acoustical signatures of the firearm and ammunition combination will be unique for each firearm and ammunition combination and the system can compare a waveform capture from each shot to discriminate between different shooters.

[0078] The system may include a shooter registration module that allows each participant to check in before a training session. Shooters may use RFID tags, biometric identification, login credentials, mobile device pairing, or some other authentication methodology to identify themselves to the system. Once registered, the system can track each shooter's performance individually and can display personalized feedback on designated portions of the screen 112 or on individual mobile devices.

[0079] FIG. 2 illustrates a process for interactive, video-based, life-fire firearms training 200. According to some embodiments, at block 202, the projector displays a target, a drill, target animation, or video scenario on the screen. Simultaneously, at block 204, thermal video is recorded on the screen. The projector and thermal camera are chosen to havecompatible horizontal field of views (HFoVs), although the camera HFoV may be slightly wider than the projector’s. In some cases, the thermal camera FOV is temporally registered to the projector image such that the X-Y coordinates of the thermal camera register with the X-Y coordinates of a projected imaged by the projector. This may be accomplished in numerous ways, as will be discussed in later detail.

[0080] At block 206, the projection and thermal video are temporally synchronized. In some cases, thermal video is captured in a separate thread or process as fast as frames are available, and each time a projection frame is rendered, the latest thermal frame is fetched. Since there may be some lag in getting thermal frames, typically on the order of 150-200ms for USB-connected cameras, for example-lag compensation can be performed.

[0081] At block 208, the projection and thermal video are spatially synchronized through a calibration procedure. Once the projector, thermal camera, and screen are positioned, a process can be performed to find a set of four corresponding points in the projection and the thermal video. These points are used to estimate the homography matrix for mapping points in the projected image into the thermal image, and vice versa. In some cases, spatial synchronization is done by using markers physically attached to the screen that are visible with the camera, which may be a multi-spectral imaging camera, and the system may synchronize one or more portions of the projection with the image captured by the camera.

[0082] In some cases, the projection points are "known" from the code generating the calibration animation. The work of calibration is finding the corresponding points in the thermal video for the projection points. Since the projected content may not be visible in the thermal video, one way to do this is to use a LWIR (black body) emitter. For instance, during a calibration process, a calibration animation is projected, and in some cases, a static rectangle in the center of the screen may suffice. The four corners of this projected rectangle are automatically saved.

[0083] While the animation is running, thermal video is automatically recorded while the emitter is manually triggered at each comer of the displayed rectangle. The emitter may be as simple as a laser pointer that fires its laser at each of the corners of the rectangle or may be a black body emitter that can be held at each corner of the rectangle to calibrate the projected image with the thermal camera field of view. The emitter may have reference markings thatcan help the emitter to coincide with markings shown in the video to ensure accurate placement of the emitter relative to the projected image.

[0084] A software labeling tool may be provided for marking the four comers in the thermal video, and the thermal points are saved and calibrated with the projected image.

[0085] In some cases, the thermal video stream may be “tuned,” or optimized, for impact detection. Most thermal cameras have on-board image processing, which may include automatic gain control (AGC), which can produce unpredictable and unstable results in the output video. In some embodiments, therefore, the raw IR sensor array values off the camera are captured, prior to on-board image processing. These sensor values may be 16-bit values which can be mapped in an 8-bit image colorspace using some sort of thresholding function. The tuning process attempts to find optimal threshold values.

[0086] At block 210, bullet impacts are detected. Bullet impacts in the target screen leave thermal signatures which persist for anywhere from a few seconds to several minutes, depending on the screen material used. In many cases, a “white hot” thermal encoding (typically the default colorization), impacts appear as small white spots against a black background in thermal video, depending on the amount of reflected IR in the scene. These white hot spots indicate a bullet impact.

[0087] At block 212, the bullet impacts are scored. Because the system has been calibrated to synchronize the projected image with the thermal camera field of view, the white hot spots indicating a bullet impact are mapped in XY space and compared to the location of the projected target in a frame of synchronized thermal video and projected video. The system can then determine exactly where in the projected image the bullet impact occurs. Moreover, the system can also detect the time at which the bullet impacted. For instance, where a shooting string must be performed within a certain time frame, the system can determine whether the impact happened within the allotted time. In some cases, a contour-based impact detection system is used to find impacts in the thermal video in realtime, which will be described hereinafter.

[0088] FIG. 3 illustrates a sample process flow 300 for contour-based impact detection, in accordance with some embodiments. At block 302, a region of interest (ROI) is determined with respect to the target screen. Impact detection is performed only within a defined region of interest in the thermal video. First, the projection corners are mapped into the thermalvideo using the homography estimated during calibration. Then the minimal enclosing, axis- aligned bounding box can be computed. Finally, the bounding box may be expanded by a configurable expansion factor to ensure that the screen is completely included in the ROI.

[0089] At block 304, impact detection begins by capturing "change" in the thermal video. This is done by computing a "difference image" by subtracting an exponentially weighted moving average (EWMA) of prior frames from the current frame. Since thermal impacts are dynamic (changing over time), the subtraction is only sensitive to pixels which get brighter, ignoring impacts which are decaying in brightness (which is a kind of change too, but not change of interest). The system can differentiate between new impacts and old impacts both by computing the difference and / or by the pseudo-coloration, with the more recent impacts showing a greater heat signature than the old impacts.

[0090] At block 306, the difference image is simplified by thresholding. A binary thresholdization is performed such that pixels below a certain intensity are set to 0. Effectively, the system effectively requires that a pixel brighten by a minimum number of intensity levels (pixel values are 0-255) to be considered as part of a "potential" impact. The brightening of a pixel is in direct relation to a heat signature associated with an impact.

[0091] At block 308, contours are determined, which are curves joining all contiguous points of the same color-white, in the binarized difference image. In general, we expect only a small number of impact contours in a single thermal frame, so the system may discard any frame with more than a specified number of contours detected because something unusual is likely occurring in such a frame, such as a person entering the scene, or a major change in reflected IR (for example, the sun coming out from behind clouds).

[0092] At block 310, the system validates the impacts as potential impacts are classified as either "true" or "false". This involves multiple steps: Contour Filtering: Contours can be accepted or rejected based on contour properties, such as area, circularity, min / mean / max pixel value within the counter, and many others. Impact Identity: Since impacts can be active in the thermal video for multiple frames, new impacts which are "close" in time (frames) and space (Euclidean pixel distance) to previously reported impacts are rejected. Impact Stability: True impacts should be more or less stable in location. Impacts which are moving (a flying piece of hot bullet jacket, for example) can be rejected. Require that an impact "persist" for a given number of frames at approximately the same location. Validated impacts can then bemapped into projection coordinates, using the homography computed during calibration, and affect the projection in subsequent frames, such as accumulating drill scoring totals or affecting game play.

[0093] FIG. 4 illustrates the system 400 in use, showing a shooter 402 aiming at a target screen 404. On the target screen 404, a projector is projecting multiple circles 406 that are moving about the screen 404. When the shooter 402 fires and the system detects a hit on one of the circles 406, the large circle 406 may become two smaller circles moving at a faster speed than the large circles. The system 400 understands the boundaries and locations of each of the projected targets, which are synchronized with the thermal images showing the impacts. In some cases, a projected target may have complex shapes identified as targets, which may move from frame to frame. The system can therefore determine whether an impact hits within a target boundary or outside a target boundary by detecting an impact and spatially and temporally associating the impact with the locations of the projected targets at the time of impact. Thus, in one example of projected targets, the shooter can practice aiming and firing at moving targets, and the targets get smaller and faster as the shooter proceed to hit the larger circles, thus changing the difficulty level as the shooter demonstrates proficiency. This concept of video-based live-fire training can be further gamified by using any desired image projection scenario. For instance, what have historically been only available as video games, first-person shooter games can be enabled that use live-fire firearms to not only play the game, but to also train for fast-paced and high-adrenaline situations.

[0094] FIG. 5 illustrates another embodiment of a system 500 for interactive, video-based, firearms training. The system 500 includes a server 502 operably coupled to a projector 504 and a camera 506 (which may be a thermal or multi-spectral camera). A target screen 508 displays an image 509 from the projector 504 and an impact detector module 510 executed by the server 502 determines an impact on the screen, and further performs a coordinate transform 512 and reconciles the coordinate transform of the impact with the coordinates of the projected image at the time of the impact. As described in relation to all the embodiments described herein, an “impact” on the screen may be a physical impact from a projectile, such as a bullet, or may be an impact from a light source, such as a laser. The camera 506 captures video of the target screen and shows an impact 514 in a pseudo-coloration as a white hot spot in the dark image of the screen in the case of thermal imaging. Once the projector 504, thermal camera 506 and target screen 508 are positioned, a calibration step may be performed to find a set of corresponding points in the projection and the thermal video image. These corresponding points are used to estimate the homography matrix for mapping points in the projected image into the thermal image, and vice versa. For clarity, homography in machine vision refers to a geometric transformation that relates two images of the same planar surface taken from different perspectives, such as from different cameras, or in this case, from a projected image onto a screen and a thermal image of the screen. Homography describes how a scene can be mapped between different viewpoints when the scene lies on a plane.

[0095] For instance, a homography can be represented by a 3x3 matrix H that transforms a point x=[x,y,l]Tin one image plane to a corresponding point x'=[x',y',l]Tin another image plane: x'=Hx

[0096] Here, H is the homography matrix, and the relationship can be expanded as:

[0097] matrix His defined up to a scale factor, meaning H has eight degrees of freedom. These degrees correspond to the ability to perform translation, rotation, scaling, and shearing transformations.

[0098] When used with embodiments described herein, homography may be used for camera calibration to map the camera field of view with the projected image, and vice versa.

[0099] For example, homography can be used to relate points in the world coordinate system to the image plane. This is essential for determining the camera's intrinsic parameters, which are used to correct for lens distortions and to understand the camera's orientation and position relative to the projected scene.

[0100] In some cases, estimation techniques are used to find a homography matrix to determine corresponding points between the projected image and the thermal image and determine the transformation that best aligns them.

[0101] In some cases, feature detection and matching is used to determine features, such as corners (e.g., Harris corners) or blobs (e.g., SIFT, SURF) in both the projected image and the camera captured image and the features are matched across images based on their respective descriptors, which capture the local image structure around the feature.

[0102] Once matches are established, a system of linear equations is set up to solve for the homography matrix H. This may be done using a method like Direct Linear Transformation (DLT).

[0103] Random sample consensus (RANSAC) may be used to robustly estimate homography by iteratively selecting random subsets of correspondences, computing the homography for each subset, and then evaluating the consensus among the full set of correspondences. This may help to minimize the influence of outliers (e.g., incorrect matches).

[0104] In some embodiments, hardware acceleration (e g., using GPUs) and using optimized algorithms enable real-time homography estimation, which can be useful in complex scenes and moving target images.

[0105] FIG. 6 illustrates a sample process flow 600 for temporal synchronization, in accordance with some embodiments. As illustrated, during sequential image frames, the system iteratively subtracts the preceding frame from the current frame and computes a difference image by subtracting an exponentially weighted moving average (EWMA) of prior frames from the current frame. Since thermal impacts are dynamic (e.g., changing over time), the subtraction is only sensitive to pixels in the image which get brighter. This allows the system to ignore impacts which are decaying in brightness.

[0106] The difference image may be simplified, in some cases, by thresholding. For example, a binary thresholdization may be performed such that pixels below a certain intensity threshold (such as 30) are set to 0 and are therefore ignored in the difference image. This effectively requires that a pixel brighten by at least 30 intensity levels to be considered as part of a potential impact.

[0107] As described above, and as with all the embodiments described herein, contour detection may be performed to further optimize the impact detection by discarding unusual frames that exhibit unexpected impact contours.

[0108] This process of impact detection may be permed between each subsequent frame, or depending on the frame rate of the camera, impact detection may be performed between a set number of frames, such as every 5thframe, every 10thframe, every 20thframe, or otherwise. Therefore, in some cases, the difference image may be determined at a set interval which may depend on the framerate. For example, where a camera has a frame rate of 120Hz, executing the impact detection process on every 5thimage frame results in an interval of every l / 24thof a second. This is typically much faster than shooters can fire their weapon and therefore still results in near real time results and data capture.

[0109] FIG. 7 illustrates spatial synchronization 700 for use with the systems described herein, in accordance with some embodiments. Spatial synchronization refers to aligning the coordinate spaces between the projected image and the camera’s field of view. This process estimates the homography matrix, described above, that correlates an impact to the screen with a location on the projected image. As illustrated, the projection screen 702 may display a projected image of a rectangle with comers 704. The projected image may be any suitable resolution, such as 1920 x 1080. The thermal camera captures images of the projection screen and the corners 704 of the rectangle are mapped in the thermal image 706. In some cases, this may be by providing a heat source on the screen 708 that aligns with the comer 704. A thermal emitter, such as a black body emitter (e.g., 2 to 20pm), can show a heat signature that aligns with the corners of the rectangle which can then be labeled in the thermal image such as by using a tool in software executed by the computing device to indicate that the thermal heat signature corresponds to a specific point in the projected image. This process may be done for multiple points around the projected image and the system can then associate the coordinates of the thermal image with the coordinates of the projected image.

[0110] The system may implement multiple registration methodologies to establish precise spatial correspondence between the thermal imaging camera field of view and the projected image coordinate space. These registration techniques encourage accurate mapping of detected impacts to their corresponding locations in the projected target space, achieving pixel, or even sub-pixel, accuracy across the entire screen area.

[0111] In some embodiments, the system employs an active thermal calibration process that may use a controlled heat source(s) to establish correspondence points betweenprojection and thermal coordinate spaces. A thermal reference emitter (136 of FIG 1), may comprise a blackbody radiation source operating in a suitable electromagnetic wavelength, such as, for example, the 8-14 pm wavelength range, is positioned at predetermined locations on the target screen 112 corresponding to known projection coordinates.

[0112] During calibration, the system projects a calibration pattern comprising fiducial markers at specific screen locations. The calibration pattern may include a grid of crosshairs, corner markers, or encoded patterns such as ArUco markers or ChArUco boards adapted for thermal visibility. An operator or automated positioning system places the thermal emitter at each fiducial marker location, creating thermal reference points visible to the thermal camera 114.

[0113] The thermal reference points appear as high-contrast spots in the thermal image, with temperatures typically 10-50°C above ambient. The system captures these thermal signatures and associates them with their corresponding projection coordinates, building a point correspondence dataset. With about four, or more, non-collinear correspondence points, the system computes a projective transformation matrix (e.g., homography) that maps any point from projection space to thermal space and vice versa.

[0114] An alternative calibration approach uses mechanical impacts at known locations to establish spatial correspondence. For example, the system projects calibration targets at predetermined screen positions. An operator fires rounds at these specific targets, creating thermal signatures at known projection coordinates.

[0115] The impact calibration module 128 detects these calibration impacts and correlates them with the projected target positions. This method advantageously uses the same thermal signature detection algorithms employed during normal operation, ensuring calibration accuracy matches operational accuracy. The system may guide the operator through the calibration sequence using visual or audio prompts, indicating which calibration target to engage next.

[0116] In some embodiments incorporating a multi-spectral camera 114 capable of capturing both visible and thermal spectra, the system may perform hybrid registration. For example, retroreflective markers may be attached to the screen at known positions that appear in both visible and thermal images when illuminated by the projector 110 and heated slightly above ambient temperature.

[0117] The hybrid registration process projects visible light patterns that create measurable thermal gradients on the screen surface. For example, high -intensity white regions in the projection cause localized heating of the screen material, creating corresponding warm areas visible in thermal imagery. By modulating the projected pattern temporally (such as flashing patterns at specific frequencies), the system differentiates projection-induced thermal signatures from other heat sources.

[0118] In some embodiments, the system implements an automated corner detection algorithm that identifies the boundaries of the projection area in the thermal image without requiring manual intervention. For example, the projector 110 displays a high-contrast border pattern that induces subtle thermal gradients at the screen edges through differential heating.

[0119] A comer detection module processes the thermal image using edge detection filters such as Sobel or Canny operators optimized for thermal imagery. The corner detection module identifies straight edge segments and computes their intersections to locate projection corners in thermal image coordinates. These detected corners correspond to known projection boundary coordinates, establishing the initial transformation parameters.

[0120] In some cases, the system may implement continuous calibration refinement during operation by analyzing impact patterns and their scored locations. When impacts occur near projected target boundaries, the system compares the detected impact position with the expected position based on whether the shot was scored as a hit or miss. Statistical analysis of multiple boundary-region impacts allows the system to detect and correct minor registration drift.

[0121] The drift correction algorithm can be used to maintain a rolling window of recent impacts and their scoring outcomes. If systematic bias is detected (for example, shots consistently scored as misses despite appearing to impact targets in the thermal view), the system adjusts the transformation matrix incrementally. These adjustments typically involve sub-pixel translations or minor rotation corrections.

[0122] In some high-precision applications, the system may employ multi-point progressive registration using 9, 16, or 25 calibration points arranged in a grid pattern. This overdetermined system allows computation of non-linear distortion corrections accounting for lens distortion in either the projector 110 or thermal camera 114.

[0123] The progressive registration process may begin with four corner points to establish the basic homography, then adds interior points to detect and correct barrel distortion, pincushion distortion, or other optical aberrations. The system can then fit a polynomial distortion model to the correspondence points, typically using Brown-Conrady or division model distortion coefficients.

[0124] In some cases, an automated registration method exploits the thermal properties of the projection system itself. Modern projectors generate waste heat that creates a thermal signature visible to the thermal camera 114. By modulating projector output in a known spatial pattern (such as sequential quadrant illumination), the system induces corresponding thermal patterns on the screen.

[0125] The auto-registration module correlates the commanded projection pattern with observed thermal changes, establishing spatial correspondence without manual intervention. This process typically requires 30-60 seconds as thermal changes propagate through the screen material. The method works particularly well with screens having moderate thermal conductivity that show clear thermal gradients.

[0126] In conjunction with any of the registration methodologies discussed, the system may perform coordinate transform, such as homography matrix computation.

[0127] In some examples, the coordinate transformation between projection and thermal spaces employs a 3><3 homography matrix H computed using the Direct Linear Transformation (DLT) algorithm:H = [hn hi2 his][h21 1122 1123][1131 1132 1]

[0128] For each correspondence point pair (xp, up) in projection space and (xt, yt) in thermal space, the system constructs linear equations: xt = (hl I xxp + hl2xyp + h 13) / (h31 xxp + h32xyp + 1) yt = (h21xxp + h22xyp + h23) / (h31 xxp + h32xyp + 1)

[0129] The system solves for the eight unknown parameters using Singular Value Decomposition (SVD) or similar numerical methods, resulting in at least four correspondence points. During operation, the impact location mapping module can transform detected impact coordinates from thermal space to projection space in real-time:[xp] [xt][yp] = HA(-1) X [yt]

[0001]

[0001]

[0130] The inverse transformation HA(-1) is pre-computed after calibration and stored in memory 106 for rapid access. The system may implement the transformation using optimized matrix operations, typically achieving sub-millisecond transformation times per impact.

[0131] The registration accuracy may be verified through a number of automated metrics. For example, the system may determine the RMS distance between predicted and actual correspondence points; the system may determine a registration consistency index indication variation in transformation accuracy across screen regions; the system may determine temporal stability by looking at the drift in registration parameters over time; and finally, the system may determine coverage uniformity by reviewing the distribution of calibration points across the screen area.

[0132] In embodiments with multiple thermal cameras 114 providing overlapping coverage, the system can perform multi-view registration. For example, each camera can undergo individual registration with the projection space, then the system computes intercamera homographies for seamless impact tracking across camera boundaries.

[0133] The multi-camera registration enables wide-area coverage using multiple lower- resolution cameras instead of a single high-resolution unit. The system can combine impact detections from multiple cameras using weighted averaging based on each camera's view angle and distance to the impact location.

[0134] In some cases, the system includes dynamic registration adjustment compensating for environmental changes affecting registration accuracy. Temperature variations causing thermal expansion of the screen frame, vibrations from repeated impacts, or settling of support structures may alter the spatial relationship between projector and camera.

[0135] In some cases, accelerometer sensors on the screen frame detect physical movement, triggering automatic recalibration if displacement exceeds thresholds. The system may perform rapid recalibration using a subset of calibration points, completing the process in under seconds during cease-fire periods.

[0136] In some cases, for regions between calibration points, the system employs spatial interpolation to maintain registration accuracy. Bilinear interpolation provides smoothtransitions between calibrated regions, while bicubic interpolation offers higher accuracy at increased computational cost.

[0137] In some examples, the adaptive interpolation module selects interpolation methods based on local registration complexity. For instance, screen regions with linear distortion may use bilinear interpolation, while areas exhibiting non-linear aberrations employ higher- order methods. This adaptive approach optimizes the balance between accuracy and computational efficiency.

[0138] The end result is a system that can display video-based images onto a projection target screen, determine impacts of bullets onto the screen, correlate the location of the impacts with the projected image, and take action based on the impacts, such as scoring the hits, updating the video image in response to the impacts, and determine the time between impacts, such as for time-based shooting drills, among other things.

[0139] In some embodiments, the system operates across multiple shooting lanes in a commercial or military training facility. A central server 102 coordinates multiple projectioncamera pairs 110, 114, each serving one or more lanes. The central server 102 synchronizes training scenarios across all lanes, enabling team-based exercises where shooters in different lanes engage coordinated threats. In some examples, multiple projection-camera pairs 110, 114 are provided to simulate an environment, such as a building, and a single shooter may move through the building, engaging multiple targets projected on multiple display screens. The multiple projection-camera pairs 110, 114 may transmit the detected impacts to a central processing system that controls the projections in response to shooter actions. A first projector-camera pair may project one or more images on a first surface and detect impacts and scoring on the first surface, while a second projector-camera pair may project one or more images on a second surface and detect impacts and scoring on the second surface. The system may thus be configured to simulate clearing a building, and may optionally allow two or more shooters to simultaneously engage the projected targets on the various surfaces in either a live-fire mode, dry -fire mode, or simultaneous mixed modes.

[0140] Each lane may include a local processing unit 104 that handles real-time impact detection and immediate feedback, while the central server 102 manages scenario progression, scoring aggregation, and performance analytics. The local units 104 may communicate with the central server 102 via high-speed communication protocol, such asethernet, fiber optic connections, or wireless communication, resulting in latency below about 10ms for synchronized multi-lane operations.

[0141] The distributed architecture enables fault tolerance, where failure of one lane's equipment doesn't affect other lanes. The system may include automatic failover mechanisms that redistribute processing load if a local unit 104 fails. In some cases, this architecture supports facilities with 10-50 lanes operating simultaneously.

[0142] In some embodiments, a portable configuration integrates all system components into a ruggedized case. The case may contain a projector 110, which may be a short-throw projector capable of creating a 2m x 1.5m image from 2m distance, a thermal camera 114, and an embedded computer 102, and may optionally include a battery power supply providing hours of operation.

[0143] In some cases, a portable screen 112 uses a collapsible frame that sets up quickly. The screen material may roll or fold for transport and may include quick-connect attachments to the frame, such as, for example, clips, hook and loop fastener, clamps, hasps or some other removable fastening system. Optional setup guides may be provided to project laser alignment patterns to ensure proper positioning of all components.

[0144] In some cases, the portable system includes cellular connectivity for cloud synchronization and remote support. In some examples, training data (e.g., DOPE) uploads automatically when internet connectivity is available. The system may operate fully offline with local storage for numerous training sessions.

[0145] In some embodiments, a competition-focused system includes match management features supporting various shooting sports formats. The system may be configured to implement official rulebooks for USPSA, IDPA, ISSF, and other organizations, automatically scoring according to competition standards.

[0146] The competition system may include competitor registration with squadding assignments, automated stage progression, real-time leaderboards, and instant replay capability for contested shots. In some cases, multiple viewing displays allow spectators to watch the action with overlay graphics showing scores and statistics. Of course, the features, such as as automated stage progression, real-time leaderboards, and multiple viewing displays are not not unique to a competition setting, but may be implemented in any of the embodiments described herein.

[0147] In some cases, the system generates official match reports compatible with other scoring systems. Video recordings of each competitor's run, along with detected impacts and timing for each impact, may archive automatically with embedded scoring data for later review or training purposes.

[0148] In some embodiments, artificial intelligence is implemented to include machine learning models that analyze shooting patterns and automatically adjust training difficulty. The Al system is configured to identify specific skill deficiencies through pattern analysis and create personalized training regimens.

[0149] The Al monitors dozens of performance metrics including draw speed, split times, transition speed, accuracy degradation under time pressure, and group size at various distances. Based on this analysis, it generates custom drills targeting identified weaknesses.

[0150] In some cases, the system includes a virtual coach that provides real-time feedback through audio cues or visual indicators. The coach adapts its communication style based on the shooter's learning preferences and response to different types of feedback.

[0151] In some examples, a tactical training embodiment creates realistic force-on-force scenarios for law enforcement and military training. The system may project photorealistic human targets, or video images from law enforcement cameras, thus exhibiting realistic. Targets may surrender, take cover, return fire (simulated), or perform other contextually appropriate actions.

[0152] The tactical system may include shoot / don't-shoot scenarios with legal and ethical decision points. Post-action reviews can analyze decision-making speed and accuracy. The system may be configured to track engagement rules compliance and can simulate less-lethal options.

[0153] Figure 8 illustrates a biometric integration and stress monitoring system 800 for correlating physiological parameters with shooting performance, in accordance with some embodiments. For instance, the system 800 my incorporate one or more comprehensive biometric monitoring capabilities that measure and analyze physiological parameters during live-fire training exercises. The biometric integration system 800 provides real-time correlation between shooter physiological state and shooting performance, enabling advanced training optimization and stress inoculation protocols.

[0154] In some cases, a wearable sensor suite 802 includes one or more physiological monitoring devices integrated into training gear. A chest-strap heart rate monitor 804 measures cardiac activity with millisecond precision, detecting both heart rate and heart rate variability (HRV). The HRV analysis module 806 computes time-domain and frequencydomain HRV metrics, including RMSSD (root mean square of successive differences) and pNN50 (percentage of successive intervals differing by more than 50ms), which indicate autonomic nervous system balance and stress response.

[0155] Galvanic skin response (GSR) sensors 808 integrated into shooting gloves or wristbands measure electrodermal activity, providing immediate indication of sympathetic nervous system activation. The GSR processing module 810 applies adaptive baseline correction to account for individual differences and environmental factors. Rapid increases in skin conductance correlate with acute stress responses, while tonic levels indicate overall arousal state.

[0156] A respiratory monitoring system 812 can employ strain gauge sensors embedded in chest straps and / or acoustic sensors detecting breathing sounds. The breathing analysis module 818 calculates respiratory rate, depth, and regularity. The system identifies respiratory patterns associated with optimal shooting performance, such as natural respiratory pause during trigger squeeze, and detects stress-induced breathing irregularities like hyperventilation or breath-holding.

[0157] An integrated accelerometer array 820 may be distributed across the shooter's body and can track postural stability and movement patterns. Alternatively, a camera based system can be used to track body landmarks and correlate motion of the body landmarks to shooting performance. In some cases, cameras and / or sensors positioned at the shoulders, hips, and weapon-holding arms can measure body sway, tremor, and recoil management. The motion analysis module 828 computes stability metrics including center of pressure displacement, sway velocity, and tremor frequency. These measurements can be correlated with shooting accuracy and identify fatigue-induced degradation.

[0158] The trigger pressure monitoring system 830 may employ a thin-film pressure sensor attached to the trigger or integrated into a trigger shoe attachment. The trigger analytics module 836 measures pull weight, pull duration, and pressure curve smoothness. The system can thus detect common trigger control errors including jerking (rapid pressurespike), slapping (excessive overtravel), and milking (grip pressure changes during trigger pull). In some cases, real-time haptic feedback 838 through a wrist-worn device guides proper trigger control.

[0159] A pupillometry system 840 integrated as a camera aimed at the shooter’s eyes, and may be contained in protective eyewear can measure pupil diameter changes. The cognitive load assessment module 842 analyzes pupil dilation patterns to infer mental workload and attention allocation. Larger pupil diameters during target presentation indicate increased cognitive processing, while constriction patterns reveal visual focus points. The system correlates pupillary responses with decision-making speed in shoot / don't-shoot scenarios.

[0160] The stress-performance correlation engine 844 synthesizes all biometric inputs to create a comprehensive stress index 846. This multi-dimensional stress score combines weighted contributions from each physiological parameter:Stressindex = wl xHRnorm w4xTremomorm + w5xPup

[0161] Where each parameter is normalized to individual baseline values and weights (wl-w5) are empirically determined or machine-learned for optimal correlation with performance degradation.

[0162] The system may implement adaptive training protocols 848 that automatically adjust scenario difficulty based on detected stress levels. When the stress index 846 exceeds predetermined thresholds, the scenario controller 850 may reduce target speed, increase target size, or extend time limits. Conversely, when physiological indicators suggest the shooter is insufficiently challenged, the system increases difficulty to maintain optimal training stress.

[0163] A stress inoculation training module 852 deliberately induces controlled stress through scenario manipulation while monitoring physiological response. The module may progressively increase stressor intensity across training sessions, building stress resilience. Stressors include time pressure, complex decision-making requirements, simulated return fire (through audio and visual effects), and competitive pressure (displaying real-time performance comparisons).

[0164] The biometric data visualization interface 862 can display real-time physiological parameters on instructor monitors 854 or AR displays. Color-coded indicators show whenparameters exceed normal ranges: green for optimal, yellow for elevated, and red for concerning levels. Historical trend graphs can be used to show physiological adaptation across training sessions, identifying improved stress management.

[0165] A performance prediction model 866 uses machine learning algorithms trained on accumulated biometric and performance data to forecast shooting accuracy based on current physiological state. The model can account for individual shooter baselines and response patterns, achieving prediction accuracy for trained individuals. Pre-shot physiological patterns can be analyzed to generate real-time performance probability scores.

[0166] The recovery assessment module 870 monitors post-shooting physiological recovery rates. Return-to-baseline times for heart rate, GSR, and breathing rate indicate fitness level and stress resilience. The system tracks recovery improvements over time, providing objective measures of training effectiveness.

[0167] Data privacy and security measures 872 protect sensitive biometric information.All physiological data can be encrypted, such as by using AES-256 encryption during transmission and storage. Access controls limit data visibility to authorized personnel.

[0168] The biometric integration system 800 interfaces with the existing training system, such as through a dedicated API 880 enabling real-time data exchange. Impact detection events trigger biometric data markers, allowing precise correlation between physiological state at the moment of trigger pull and shot placement. This correlation enables identification of optimal pre-shot physiological states for each individual shooter.

[0169] A coaching recommendation engine 884 may analyze patterns in biometricperformance correlations to generate personalized training advice. Recommendations may include breathing exercises for shooters showing respiratory irregularities, grip adjustments for those with excessive tremor, or stress management techniques for shooters showing performance degradation under pressure.

[0170] The system includes baseline establishment protocols 892 that measure individual physiological parameters during rest and various activity levels. These baselines account for individual differences in resting heart rate, skin conductance, and other metrics. Baseline measurements are updated periodically to account for fitness changes and environmental adaptation. The system may also output performance reports 874 that indicate current score, stress levels, which may be tracked over time to show stress / score trends. The system mayinclude one or more processors and one or more non-transitory computer readable media that may store various modules, applications, programs, or other data. The computer-readable media may include instructions that, when executed by the one or more processors, cause the processors to perform the operations described herein for the system.

[0171] In some embodiments, the system incorporates a dual-mode training capability that enables both live-fire training with conventional ammunition and dry -fire training using laser-equipped firearms. Of course, it should be appreciated that the live-fire and dry -fire systems may be completely separate and independent systems, they can also be incorporated into a single, dual -mode, system, in which the required hardware (e.g., projector, imaging sensors, processor(s), etc.) can be contained in a single housing. In some cases, the dualmode system leverages the same projection infrastructure and may utilize the same detection hardware, or may incorporate specific detectors for detecting a laser impact on the ballistic screen and the thermal signature from a bullet impact, while implementing mode-specific detection algorithms optimized for the distinct physical characteristics of bullet impacts versus laser emissions.

[0172] In the dry-fire configuration, training firearms are equipped with laser emission modules that generate brief infrared laser pulses when the trigger is actuated. In some cases, a firearm simulator (e.g., training weapon) may emit a light pulse in the 780nm spectrum. Some suitable firearm simulators rely on CO2 or green gas to simulate firearm recoil, and pulling the trigger strikes a capsule that vibrates to activate a laser beam that emits from the barrel. Other firearm simulators may be electronic systems that generate the beam through an electronic switch upon trigger pull. Still other firearm simulators rely on a real firearm and place a laser module into the chamber that is activated upon trigger pull and an impact by the firing pin. In any event, the laser modules typically emit in the near-infrared (NIR) spectrum at wavelengths between 780-950 nanometers (SWIR), which differs substantially from the long-wave infrared (LWIR) thermal signatures at 8,000-14,000 nanometers detected during live-fire training. This spectral separation enables the system to distinguish between training modes and apply appropriate detection algorithms.

[0173] One of the differences between live-fire and dry-fire detection relates to the temporal and thermal characteristics of the detected events. In live-fire mode, bullet impacts create persistent thermal signatures through kinetic energy conversion, generating localizedheating that typically persists for 1-300 seconds depending on screen material properties. The thermal bloom exhibits a characteristic rise time of milliseconds to peak temperature, followed by exponential decay as heat dissipates through conduction and radiation. In contrast, laser pulses in dry -fire mode create transient optical signatures lasting only for the duration of the laser pulse, typically 1-100 milliseconds, without generating measurable thermal effects on the screen material.

[0174] In some embodiments, the detection algorithm automatically configures image processing parameters based on the selected training mode. In dry-fire mode, the system may implement one or more modifications to the impact detection pipeline. For instance, the temporal stability filter, which in live-fire mode looks at impacts that persist at the same location for at least two consecutive frames to reject hot flying debris and other anomalies, may be disabled or significantly relaxed for a dry-fire operating mode. This modification may be performed because laser signatures may appear in only a single video frame when operating at typical frame rates of 30-60 frames per second, where each frame represents 16.7-33.3 milliseconds of capture time.

[0175] A laser streak detection module may address the unique challenge of laser movement during pulse emission. Unlike bullets that impact at a discrete point regardless of muzzle movement, an active laser traces a path across the screen if the firearm moves during the pulse duration. The system can analyze detected bright regions in each frame to identify elongated patterns indicative of laser movement. The system may then compute the major and minor axes of detected contours, such as by using moment analysis:Orientation = 0.5 x arctan(2*pl l / (p20-p02))Eccentricity = sqrt(l - (minor_axis / major_axis)2)

[0176] Where p represents the image moments. Contours with eccentricity exceeding 0.7 may be classified as streaks rather than point impacts.

[0177] In some cases, a temporal-spatial blackout filter prevents multiple detections from a single laser activation event. Upon detecting a laser signature, temporal-spatial blackout filter may establish an exclusion zone extending about 10-20 pixels radially from the detection center and persisting for 4-6 frames (approximately 65-200 milliseconds at 30 fps). Any potential detections within this exclusion zone may be suppressed, eliminating falsemultiple impacts from laser streak continuation across frame boundaries or muzzle movement during the pulse.

[0178] In some cases, a multi-spectral detection system can be used to simultaneously detect bullet impacts and laser impacts and may optimize sensor configuration for dual-mode operation. For example, in embodiments utilizing separate sensors, a beam splitter can be used to direct long-wave infrared radiation to a thermal sensor while near-infrared radiation passes to a separate NIR-sensitive sensor, such as a silicon-based CMOS or CCD camera. In embodiments using a single broadband sensor, one or more spectral discrimination filters can select between LWIR and NIR bands, or the sensor simultaneously captures both spectra with algorithmic separation.

[0179] Of course, completely separate detectors may be used within the system and the system can be configured for live-fire or dry-fire as desired, and the appropriate imaging sensor is activated.

[0180] The laser pulse characterization module measures temporal and spatial properties of detected laser signatures to validate authenticity and reject spurious detections. Valid laser pulses exhibit specific characteristics, such as, for example, rise time under 1 millisecond (limited by camera frame rate), pulse duration (e g., between 1-100 milliseconds depending on the laser module design), and spatial extent consistent with laser spot size at the detected range. The laser pulse characterization module may maintain a signature library of known laser training devices, enabling device-specific optimization and counterfeit detection.

[0181] In some cases, a calibration adaptation system may account for the different optical properties of laser versus thermal detection. While thermal calibration uses heat sources to establish correspondence points, laser calibration employs visible or NIR laser pointers to mark calibration positions. The dual calibration matrix storage can maintain separate homography transformations for thermal and optical detection modes, accounting for potential differences in sensor positioning, lens distortion, and spectral focal plane variations.

[0182] In some cases, a frame rate optimization module can dynamically adjust capture parameters based on training mode. In live-fire mode, frame rates of 9-30 fps sufficiently capture thermal signature evolution. However, dry -fire mode benefits from higher frame rates, such as 60-120 fps or more to reliably capture brief laser pulses and reduce theprobability of pulse occurrence at frame boundaries. The frame rate optimization module can balance frame rate against processing load and detection reliability.

[0183] In some embodiments, the dual-mode system enables simultaneous live-fire and dry -fire training, allowing mixed training scenarios where some participants use live ammunition while others use laser-equipped firearms. For example, when conducting firearm drills that require the shooter to draw their weapon from a holster, those with limited training in holster drawing may use a laser firearm to reduce the consequences of improper holster draw. In this case where simultaneous shooters are using live-fire and dry -fire, the mode discrimination algorithm analyzes each detected event's spectral content, temporal persistence, and thermal signature to classify it as either a bullet impact or laser pulse. This classification enables proper scoring and feedback for each participant regardless of training mode.

[0184] In some cases, the system can adjust detection parameters based on ambient lighting conditions that particularly affect laser detection. High ambient infrared levels from sunlight or artificial lighting can reduce laser contrast. In this case, the system can implement adaptive thresholding that continuously adjusts detection sensitivity based on measured background levels. In extreme cases, the system may recommend transitioning to alternative wavelengths or modulated laser encoding for improved discrimination.

[0185] In some implementations, the processor(s) may include a central processing unit (CPU), a graphical processing unit (GPU), both CPU and GPU, a microprocessor, a digital signal processor or other processing units or components known in the art. Alternatively, or in addition, the functionally described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), etc. Additionally, each of the processor(s) may possess its own local memory, which alsomay store program modules, program data, and / or one ormore operating systems. The one or more control systems, computer controller and remote control, may include one or more cores.

[0186] Embodiments may be provided as a computer program product including a non- transitory machine-readable storage medium having stored thereon instructions (in compressed or uncompressed form) that may be used to program a computer (or other electronic device) to perform processes or methods described herein. The computer-readable media may include volatile and / or nonvolatile memory, removable and non-removable media implemented in any method or technology for storage of information, such as computer- readable instructions, data structures, program modules, or other data. The machine-readable storage medium may include, but is not limited to, hard drives, floppy diskettes, optical disks, CD-ROMs, DVDs, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, flash memory, magnetic or optical cards, solid-state memory devices, or other types of media / machine-readable medium suitable for storing electronic instructions. Further, embodiments may also be provided as a computer program product including a transitory machine-readable signal (in compressed or uncompressed form). Examples of machine-readable signals, whether modulated using a carrier or not, include, but are not limited to, signals that a computer system or machine hosting or running a computer program can be configured to access, including signals downloaded through the Internet or other networks.

[0187] A person of ordinary skill in the art will recognize that any process or method disclosed herein can be modified in many ways. The process parameters and sequence of the steps described and / or illustrated herein are given by way of example only and can be varied as desired. For example, while the steps illustrated and / or described herein may be shown or discussed in a particular order, these steps do not necessarily need to be performed in the order illustrated or discussed.

[0188] The various exemplary methods described and / or illustrated herein may also omit one or more of the steps described or illustrated herein or comprise additional steps in addition to those disclosed. Further, a step of any method as disclosed herein can be combined with any one or more steps of any other method as disclosed herein.

[0189] The disclosure sets forth example embodiments and, as such, is not intended to limit the scope of embodiments of the disclosure and the appended claims in any way. Embodiments have been described above with the aid of functional building blocks illustrating the implementation of specified functions and relationships thereof. Theboundaries of these functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternate boundaries can be defined to the extent that the specified functions and relationships thereof are appropriately performed.

[0190] The foregoing description of specific embodiments will so fully reveal the general nature of embodiments of the disclosure that others can, by applying knowledge of those of ordinary skill in the art, readily modify and / or adapt for various applications such specific embodiments, without undue experimentation, without departing from the general concept of embodiments of the disclosure. Therefore, such adaptation and modifications are intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. The phraseology or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of the specification is to be interpreted by persons of ordinary skill in the relevant art in light of the teachings and guidance presented herein.

[0191] The breadth and scope of embodiments of the disclosure should not be limited by any of the above-described example embodiments, but should be defined only in accordance with the following claims and their equivalents.

[0192] Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain implementations could include, while other implementations do not include, certain features, elements, and / or operations. Thus, such conditional language generally is not intended to imply that features, elements, and / or operations are in any way required for one or more implementations or that one or more implementations necessarily include logic for deciding, with or without user input or prompting, whether these features, elements, and / or operations are included or are to be performed in any particular implementation.

[0193] Unless otherwise noted, the terms “connected to” and “coupled to” (and their derivatives), as used in the specification, are to be construed as permitting both direct and indirect (i.e., via other elements or components) connection. In addition, the terms “a” or “an,” as used in the specification, are to be construed as meaning “at least one of.” Finally, for ease of use, the terms “including” and “having” (and their derivatives), as used in the description do not preclude additional components and are to be construed as open ended.

[0194] The specification and annexed drawings disclose examples of systems, apparatus, devices, and techniques that may provide a system and method for a system for interactive, video-based, firearms training. It is, of course, not possible to describe every conceivable combination of elements and / or methods for purposes of describing the various features of the disclosure, but those of ordinary skill in the art recognize that many further combinations and permutations of the disclosed features are possible. Accordingly, various modifications may be made to the disclosure without departing from the scope or spirit thereof. Further, other embodiments of the disclosure may be apparent from consideration of the specification and annexed drawings, and practice of disclosed embodiments as presented herein. Examples put forward in the specification and annexed drawings should be considered, in all respects, as illustrative and not restrictive. Although specific terms are employed herein, they are used in a generic and descriptive sense only, and not used for purposes of limitation.

[0195] Those skilled in the art will appreciate that, in some implementations, the functionality provided by the processes and systems discussed above may be provided in alternative ways, such as being split among more software programs or routines or consolidated into fewer programs or routines. Similarly, in some implementations, illustrated processes and systems may provide more or less functionality than is described, such as when other illustrated processes instead lack or include such functionality respectively, or when the amount of functionality that is provided is altered. In addition, while various operations may be illustrated as being performed in a particular manner (e g., in serial or in parallel) and / or in a particular order, those skilled in the art will appreciate that in other implementations the operations may be performed in other orders and in other manners. Those skilled in the art will also appreciate that the data structures discussed above may be structured in different manners, such as by having a single data structure split into multiple data structures or by having multiple data structures consolidated into a single data structure. Similarly, in some implementations, illustrated data structures may store more or less information than is described, such as when other illustrated data structures instead lack or include such information respectively, or when the amount or types of information that is stored is altered. The various methods and systems as illustrated in the figures and described herein represent example implementations. The methods and systems may be implemented in software, hardware, or a combination thereof in other implementations. Similarly, theorder of any method may be changed, and various elements may be added, reordered, combined, omitted, modified, etc., in other implementations.

[0196] From the foregoing, it will be appreciated that, although specific implementations have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit and scope of the appended claims and the elements recited therein. In addition, while certain aspects are presented below in certain claim forms, the inventors contemplate the various aspects in any available claim form. For example, while only some aspects may currently be recited as being embodied in a particular configuration, other aspects may likewise be so embodied. Various modifications and changes may be made as would be obvious to a person skilled in the art having the benefit of this disclosure. It is intended to embrace all such modifications and changes and, accordingly, the above description is to be regarded in an illustrative rather than a restrictive sense.

[0197] The following clauses also form a part of the description.

[0198] Clause 1 : A system for firearms training, comprising a projector; an image capture device;

[0199] a target screen; and a computing device operably coupled to the projector and image capture device and configured with instructions that, when executed, cause the computing device to: display an image onto the target screen; detect, using the image capture device, an impact on the target screen; and synchronize a location of the impact on the target screen with a location in the image displayed on the target screen.

[0200] Clause 2: The system as in clause 1, wherein the image capture device is a thermal imaging camera configured to capture a thermal image of the target screen.

[0201] Clause 3: A method for video-based, live-fire firearms training, comprising displaying a target image onto a target screen to generate a projected target; capturing images of the target screen and projected target; temporally synchronizing the projected target and the captured images; spatially synchronizing the projected target and the captured images; detecting impacts on the target screen; and scoring the impacts by correlating a location of the bullet impacts on the target screen with a location of the projected target at a time of the bullet impact.

[0202] Clause 4: A computer-readable medium storing instructions that, when executed by one or more processors, cause a live-fire training system to calibrate spatialcorrespondence between a projection system and a thermal imaging system using a homography estimation; project interactive targets onto a ballistic screen; detect bullet impacts on the ballistic screen using thermal imaging; correlate impact locations with projected target positions in real-time; update target behavior based on detected impacts; and track and store performance metrics.

[0203] Clause 5: The computer-readable medium of clause 4, wherein the instructions further cause the system to implement safety protocols including automatic cessation upon detecting impacts outside designated areas; monitor shooter positions to ensure safe firing angles; and interface with range safety systems for emergency shutdown.

[0204] Clause 6: A ballistic target screen for live-fire training, comprising a front layer comprising self-healing polymer material between 3 and 25 millimeters thick; and a backing layer providing structural support to the front layer; wherein the self-healing polymer material generates thermal signatures persisting between 2 and 300 seconds upon bullet impact.

[0205] Clause 7: The ballistic target screen of clause 6, wherein the polymer material comprises at least one of polyurethane or poly urea compounds optimized for thermal signature generation and rapid heat dissipation.

[0206] Clause 8: A dual-mode firearms training apparatus comprising the apparatus of any of the disclosed embodiments; a laser detection subsystem comprising: a silicon photodetector array sensitive to 780-950 nanometer wavelength; a high-speed digitizer sampling at least 1000 frames per second; and a mode selection switch configuring the processing unit to: in live-fire mode: detect thermal signatures persisting 2-300 seconds using the thermal detection unit; in dry-fire mode: detect optical pulses lasting 1-100 milliseconds using the laser detection subsystem; and in hybrid mode: simultaneously process both thermal and optical signatures; wherein the processing unit applies distinct detection algorithms optimized for the temporal and spectral characteristics of each mode.

[0207] Clause 9: A method of operating the dual-mode apparatus of clause 8, comprising: receiving a mode selection input indicating dry -fire training mode; disabling temporal stability filters requiring multi-frame persistence; enabling a spatial-temporal exclusion filter that: creates a 20-pixel radius exclusion zone around each detected laser signature; maintains the exclusion zone for 4-6 consecutive frames; and rejects subsequent detections within theexclusion zone; detecting laser streaks by computing contour eccentricity and classifying contours with eccentricity greater than 0.7 as movement artifacts; and scoring laser impacts using the geometric centroid of detected laser signatures.

Claims

CLAIMSWhat is claimed is:

1. A live-fire firearms training system comprising: a ballistic target screen comprising a polymer material configured to generate thermal signatures upon bullet impact; a projector configured to display moving target images onto the ballistic target screen; a thermal imaging camera configured to capture thermal images of the ballistic target screen; one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to: project a sequence of target images onto the ballistic target screen; capture thermal images of the ballistic target screen during live-fire shooting; detect thermal signatures of bullet impacts in the captured thermal images; temporally synchronize the projected target images with the captured thermal images; spatially synchronize locations in the projected target images with corresponding locations in the captured thermal images; determine whether detected bullet impacts coincide with projected targets based on the spatial and temporal synchronization; and modify subsequent projected target images based on the determined bullet impacts.

2. The system of claim 1, wherein the instructions further cause the system to: identify multiple simultaneous shooters based on at least one of temporal patterns of impacts, spatial clustering of impacts, thermal signature characteristics, or acoustical signatures; and maintain separate scoring for each identified shooter.

3. The system of claim 2, wherein identifying multiple simultaneous shooters comprises:defining virtual shooting lanes on the ballistic target screen; associating impacts with specific lanes based on impact location; and tracking performance metrics separately for each lane.

4. The system of claim 1, wherein detecting thermal signatures comprises: computing an exponentially weighted moving average (EWMA) of prior thermal frames; subtracting the EWMA from a current thermal frame to generate a difference image; applying adaptive thresholding to the difference image based on measured background noise to generate a thresholded image; and identifying contours in the thresholded image that satisfy predetermined validation criteria.

5. The system of claim 4, wherein the predetermined validation criteria comprise: a contour area between 10 and 500 pixels; a contour circularity greater than 0.6; temporal persistence across at least 2 consecutive frames; and spatial stability with movement less than 5 pixels between frames.

6. The system of claim 1, wherein the polymer material comprises one or more of embedded microcapsules containing a healing agent and a polymer matrix that reforms chemical bonds after bullet passage.

7. The system of claim 1, wherein temporally synchronizing comprises: timestamping each projected frame and each captured thermal frame; measuring system latency during calibration; and applying lag compensation based on the measured latency.

8. The system of claim 1, wherein spatially synchronizing comprises: projecting a calibration pattern onto the ballistic target screen to indicate a projection space;identifying corresponding points in the projection space and a thermal image space using a thermal emitter; computing a homography matrix based on the corresponding points; and applying the homography matrix to map impact locations between coordinate spaces.

9. The system of claim 1, wherein modifying subsequent projected target images comprises at least one of: changing target size based on shooting accuracy; adjusting target movement speed based on hit rate; splitting targets into multiple smaller targets upon impact; or introducing new targets based on performance metrics.

10. A method for interactive live-fire firearms training, comprising: displaying a video sequence comprising moving targets onto a ballistic screen using a projector; continuously capturing thermal video of the ballistic screen during live-fire shooting using a thermal imaging camera; computing difference images between sequential thermal video frames to identify pixels exhibiting increasing thermal intensity indicative of bullet impacts; applying threshold filtering to the difference images to generate binary images; detecting contours in the binary images corresponding to bullet impacts; validating detected contours based on area, circularity, and temporal persistence; mapping validated impact locations from thermal image coordinates to projected image coordinates using a pre-calibrated homography matrix; and scoring impacts based on correspondence with target locations in synchronized projected frames.

11. The method of claim 10, further comprising dynamically adjusting target difficulty based on scoring results.

12. The method of claim 10, further comprising: registering multiple shooters before a training session; assigning each shooter to a designated region of the ballistic screen; associating detected impacts with specific shooters based on impact location; and providing individualized feedback to each shooter.

13. The method of claim 10, wherein computing difference images comprises: maintaining an exponentially weighted moving average (EWMA) of thermal frame intensities; updating the EWMA with an adaptive weighting factor based on scene dynamics; and computing pixel-wise differences between current frames and the EWMA.

14. The method of claim 10, further comprising: detecting environmental conditions including ambient temperature and lighting; adjusting detection thresholds based on the detected environmental conditions; and modifying projector settings to maintain target visibility.

15. The method of claim 10, wherein validating detected contours comprises: filtering contours based on geometric properties; tracking contours across multiple frames to verify persistence; and confirming impact authenticity.

16. The method of claim 10, further comprising: storing impact data including location, time, and shooter identification; analyzing shooting patterns to identify training needs; generating performance reports with improvement recommendations; and comparing performance across multiple training sessions.

17. A method for video-based, live-fire firearms training, comprising:displaying a target image onto a target screen to generate a projected target; capturing images of the target screen and projected target; temporally synchronizing the projected target and the captured images; spatially synchronizing the projected target and the captured images; detecting a bullet impact on the target screen; and scoring the bullet impact by correlating a location of the bullet impact on the target screen with a location of the projected target at a time of the bullet impact.

18. The method of claim 17, wherein detecting impacts on the target screen comprises: capturing thermal images of the target screen using a thermal imaging camera; identifying thermal signatures having temperatures 20-80°C above ambient temperature within the captured thermal images; validating the thermal signatures by confirming persistence of the thermal signatures at a consistent location for at least 2 consecutive frames with spatial displacement less than 5 pixels between frames; and distinguishing new impacts from decaying impacts by analyzing temporal decay profiles of the thermal signatures over a 1-5 second window.

19. The method of claim 17, wherein spatially synchronizing the projected target and the captured images comprises: projecting a calibration pattern containing at least four fiducial markers at predetermined coordinates on the target screen; positioning a thermal emitter generating 8-14 micrometer wavelength radiation at each fiducial marker location; detecting thermal signatures from the thermal emitter in the captured images; establishing point correspondences between the predetermined coordinates and detected thermal signature locations; computing a homography transformation matrix using the point correspondences; and applying the homography transformation matrix to map impact locations from captured image coordinates to projected target coordinates with sub-pixel accuracy.

20. The method of claim 17, further comprising: identifying multiple simultaneous shooters by analyzing at least one of: temporal patterns between consecutive impacts wherein each shooter exhibits a characteristic firing cadence; spatial clustering of impacts wherein each shooter produces a distinct grouping pattern with measurable mean point of impact and standard deviation; or thermal signature characteristics wherein different firearm and ammunition combinations produce distinguishable peak temperatures and cooling rates; assigning each detected impact to a specific shooter based on the analysis; maintaining separate scoring records for each identified shooter; and displaying individualized performance feedback on designated regions of the target screen corresponding to each shooter's assigned lane.

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