A shot detection system can include a shooting target that is made of paper and printed to include fiducials, borders and a bar code together with a scoring region and markings. A method of camera adjustment control can find and lock onto a target. A target acquisition procedure can include extracting a region of interest (image of the shooting target), calibrating the region of interest and then target locking. The real-time target locking procedure can include correcting an image of the region of interest to account for tilt, rotation and skew. A methodology of shot detection can include acquiring probable and confirming shot candidates and then verifying the probably shot candidate with the confirming shot candidates to substantially eliminate falsely identified shots candidates, thereby significantly improving performance and experience.

An apparatus and method for displaying an operational area to an operative of a host platform, said operational area being defined within an external real-world environment relative to said host platform, the apparatus comprising a viewing device (12) configured to provide to said operative, in use, a three-dimensional view of said external real-world environment, a display generating device for creating images at the viewing device, and a processor (32) comprising an input (34) for receiving real-time first data representative of a specified target and its location within said external real-world environment and configured to receive or obtain second data representative of at least one characteristic of said specified target, the processor (32) being further configured to: use said first and second data to calculate a geometric volume representative of a region of influence of said specified target relative to said real-world external environment and/or said host platform, generate three-dimensional image data representative of said geometric volume, and display a three dimensional model, depicting said geometric volume and created using said three-dimensional image data, on said display generating device for creating images at the viewing device, the apparatus being configured to project or blend said three-dimensional model within said view of said external real-world environment at the relative location therein of said specified target.

A method is provided for detecting and tracking targets in a series of images, at least one track as the existing track, being formed from spots detected in images in the series preceding a current image. The method includes, for the current image: obtaining at least one spot included in the current image and, for each spot, a value representing a characteristic of the spot; classifying each spot according to the representative value in a first category when the representative value is higher than a first predefined threshold or in a second category when the representative value lies between the first predefined threshold and a lower second predefined threshold; for each existing track, allocating a spot in the first category to the existing track and allocating a spot in the second category to the existing track when no spot in the first category is compatible with said existing track.

A method, system, and article is directed to geolocation and attitude correction for mobile rolling shutter cameras.

Methods and systems fort operating one or more light sources to project adversarial patterns generated to disorient a machine learning based detection system, comprising generating one or more adversarial patterns configured to disorient the machine learning based detection system and operating one or more light sources configured to project one or more of the adversarial pattern(s) in association with the targeted object in order to disorient the machine learning based detection system.

A method for assisting the location of a target for a first user equipped with an observation device includes an augmented reality observation device associated with a first user reference frame. According to this method, a reference platform associated with a master reference frame is positioned on the terrain, the reference platform is observed from at least one camera worn by the first user, the geometry of the observed platform is compared with a numerical model of same and the orientation and location of the first user reference frame is deduced with respect to the master reference frame. It is then possible to display, on an augmented reality observation device, at least one virtual reticle locating the target.

Discussed herein are architectures and techniques for improving execution or training of machine learning techniques. A method can include receiving a request for image data, the request indicating an analysis task to be performed using the requested image data, determining a minimum image quality score for performing the analysis task, issuing a request for image data associated with an image quality at last equal to, or greater than, the determined minimum image quality score, receiving, in response to the request, image data with an image quality score greater than, or equal to, the determined minimum image quality score, and providing the received image data to (a) a machine learning (ML) model executor to perform the image analysis task or (b) an ML model trainer that trains the ML model to perform the image analysis task.

A system for performing object identification combines pose determination, EO/IR sensor data, and novel computer graphics rendering techniques. A first module extracts the orientation and distance of a target in a truth chip given that the target type is known. A second is a module identifies the vehicle within a truth chip given the known distance and elevation angle from camera to target. Image matching is based on synthetic image and truth chip image comparison, where the synthetic image is rotated and moved through a 3-Dimensional space. To limit the search space, it is assumed that the object is positioned on relatively flat ground and that the camera roll angle stays near zero. This leaves three dimensions of motion (distance, heading, and pitch angle) to define the space in which the synthetic target is moved. A graphical user interface (GUI) front end allows the user to manually adjust the orientation of the target within the synthetic images. The system also includes the generation of shadows and allows the user to manipulate the sun angle to approximate the lighting conditions of the test range in the provided video.

A firearm system includes a firearm and a computer. Electronics in the firearm determine data that includes a pathway between different points of aim of the firearm as the firearm moves. The computer receives this data and builds an image of the pathway between the different points of aim of the firearm.

Systems, methods, and apparatus for identifying and tracking UAVs including a plurality of sensors operatively connected over a network to a configuration of software and/or hardware. Generally, the plurality of sensors monitors a particular environment and transmits the sensor data to the configuration of software and/or hardware. The data from each individual sensor can be directed towards a process configured to best determine if a UAV is present or approaching the monitored environment. The system generally allows for a detected UAV to be tracked, which may allow for the system or a user of the system to predict how the UAV will continue to behave over time. The sensor information as well as the results generated from the systems and methods may be stored in one or more databases in order to improve the continued identifying and tracking of UAVs.