Abstract: A system is provided. The system includes a first platform including a first platform level agent configured to direct one or more actions of the first platform based on at least one of a selected target or a selected goal. The system also includes a computer system in communication with the first platform level agent. The computer system programmed to a) execute a supervisor level agent configured to select at least one of a target or a goal for one or more platforms including the first platform, b) receive targeting information including one or more targets, c) receive platform information for the one or more platforms, d) select, by the supervisor level agent, a target of the one or more targets based on the target information and the platform information, and e) transmit, to the first platform level agent, the selected target.

Abstract: A method includes receiving, by machine-learning logic, observations indicative of a states associated with a first and second group of vehicles arranged within an engagement zone during a first interval of an engagement between the first and the second group of vehicles. The machine-learning logic determines actions based on the observations that, when taken simultaneously by the first group of vehicles during the first interval, are predicted by the machine-learning logic to result in removal of one or more vehicles of the second group of vehicles from the engagement zone during the engagement. The machine-learning logic is trained using a reinforcement learning technique and on simulated engagements between the first and second group of vehicles to determine sequences of actions that are predicted to result in one or more vehicles of the second group being removed from the engagement zone. The machine-learning logic communicates the plurality of actions to the first group of vehicles.

Abstract: Apparatus and methods for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft are described. Training of the MLA can include: the MLA determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action from the MLA; after sending the first-aircraft action, receiving an observation of the environment and a reward signal at the MLA, the observation including information about the environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action, the reward signal indicating a score of performance of the first-aircraft action based on dynamic and kinematic properties of the second aircraft; and updating the MLA based on the observation of the environment and the reward signal.

Abstract: A method includes obtaining multiple sets of trajectory data, each descriptive of trajectories of two or more objects (e.g., first and second objects). The method also includes generating transformed trajectory data based on the trajectory data. Each set of transformed trajectory data is descriptive of the trajectories of the two or more objects in a normalized reference frame in which a movement path of the first object is constrained. The method further includes generating feature data, performing a clustering operation based on the feature data to generate a set of trajectory clusters, and generating training data based on the set of trajectory clusters. The method further includes using the training data to train a machine learning classifier to classify particular trajectory patterns.

Abstract: Described is a system for real-time object recognition. The system extracts a candidate target region representing a candidate object from an input image of a scene based on agglomeration of channel features. The candidate target region is classified using a trained convolutional neural network (CNN) classifier, resulting in an initial classified object. A multi-target tracker is used for tracking the classified objects for final classification of each classified object, resulting in a final output, and a device is controlled based on the final output.

Abstract: Described is a system for autonomous behavior generation. The system includes both a high-level controller and a low-level controller. The high-level controller receives observations from an environment and, using a neural net, selects a high-level behavior based on the observations from the environment. The low-level controller generates an output command for a scripted action based on the selected one high-level behavior. After generating the output command, the system can implement an action, such as causing a device to perform the scripted action.

Abstract: Described is a system for visual activity recognition that includes one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations including detecting a set of objects of interest in video data and determining an object classification for each object in the set of objects of interest, the set including at least one object of interest. The one or more processors further perform operations including forming a corresponding activity track for each object in the set of objects of interest by tracking each object across frames. The one or more processors further perform operations including, for each object of interest and using a feature extractor, determining a corresponding feature in the video data. The system may provide a report to a user's cell phone or central monitoring facility.

Abstract: A device includes a control input generator and a neural network trainer. A flight simulator is configured to generate first state data responsive to a first control input from the control input generator and to provide the first state data to a first neural network to generate a candidate second control input. The control input generator is also configured to select, based on a random value, a second control input from between the candidate second control input and a randomized offset control input that is based on a random offset applied to the first control input. The flight simulator is configured to generate second state data responsive to the second control input from the control input generator. The neural network trainer is configured to update weights of the first neural network based, at least in part, on the first state data and the second state data.

Abstract: Described is a system for converting a convolutional neural network (CNN) designed and trained for color (RGB) images to one that works on infrared (IR) or grayscale images. The converted CNN comprises a series of convolution layers of neurons arranged in a set kernels having corresponding depth slices. The converted CNN is used for performing object detection. A mechanical component of an autonomous device is controlled based on the object detection.

Abstract: Described is a system for visual activity recognition. In operation, the system detects a set of objects of interest (OI) in video data and determines an object classification for each object in the set of OI, the set including at least one OI. A corresponding activity track is formed for each object in the set of OI by tracking each object across frames. Using a feature extractor, the system determines a corresponding feature in the video data for each OI, which is then used to determine a corresponding initial activity classification for each OI. One or more OI are then detected in each activity track via foveation, with the initial object detection and foveated object detection thereafter being appended into a new detected-objects list. Finally, a final classification is provided for each activity track using the new detected-objects list and filtering the initial activity classification results using contextual logic.

Abstract: A method includes generating, based on first surface data that includes three-dimensional (3D) point positions corresponding to a first portion of a surface of an object, first hole data that indicates first positions of holes in the first portion of the surface. The method includes generating, based on second surface data corresponding to a second portion of the surface of the object, second hole data that indicates second positions of the holes in the second portion of the surface. The method also includes matching the first positions to the second positions to perform an alignment with respect to the first surface data and the second surface data.

Abstract: Apparatus and methods for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft are described. Training of the MLA can include: the MLA determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action from the MLA; after sending the first-aircraft action, receiving an observation of the environment and a reward signal at the MLA, the observation including information about the environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action, the reward signal indicating a score of performance of the first-aircraft action based on dynamic and kinematic properties of the second aircraft; and updating the MLA based on the observation of the environment and the reward signal.

Abstract: A system and method of automated classification of rock types includes: partitioning, by a processing device, an image into partitions; extracting, by the processing device, sub-images from each of the partitions; first-level classifying, by an automated classifier, the sub-images into corresponding first classes; and second-level classifying, by the processing device, the partitions into corresponding second classes by, for each partition of the partitions, selecting a most numerous one of the corresponding first classes of the sub-images extracted from the partition. A method of displaying automated classification results on a display device is provided. The method includes: receiving, by a processing device, an image partitioned into partitions and classified into corresponding classes; and manipulating, by the processing device, the display device to display the image together with visual identification of the partitions and their corresponding classes.

Abstract: Described is a learning system for multi-agent applications. In operation, the system initializes a plurality of learning agents. The learning agents include both tactical agents and strategic agents. The strategic agents take an observation from an environment and select one or more of the tactical agents to produce an action that is used to control a platform's actuators or simulated movements in the environment to complete a task. Alternatively, the tactical agents produce the action corresponding to a learned low-level behavior to control the platform's actuators or simulated movements in the environment to complete the task.

Abstract: Described is an object recognition system. Using an integral channel features (ICF) detector, the system extracts a candidate target region (having an associated original confidence score representing a candidate object) from an input image of a scene surrounding a platform. A modified confidence score is generated based on a location and height of detection of the candidate object. The candidate target regions are classified based on the modified confidence score using a trained convolutional neural network (CNN) classifier, resulting in classified objects. The classified objects are tracked using a multi-target tracker for final classification of each classified object as a target or non-target. If the classified object is a target, a device can be controlled based on the target.

Abstract: The present invention relates to a surveillance system and, more particularly, to a graphical display and user interface system that provides high-speed triage of potential items of interest in imagery. The system receives at least one image of a scene from a sensor. The image is pre-processed to identify a plurality of potential objects of interest (OI) in the image. The potential OI are presented to the user as a series of chips on a threat chip display (TCD), where each chip is a region extracted from the image that corresponds to a potential OI. Finally, the system allows the user to designate, via the TCD, any one of the chips as an actual OI.

Abstract: An apparatus includes a nearest neighbor search engine configured to receive multiple sets of surface data. Each of the multiple sets includes three-dimensional point positions of a corresponding portion of a surface of an object. The nearest neighbor search engine is configured to perform a nearest neighbor search based on adjacency data indicating pairs of overlapping sets of the multiple sets to identify, in each pair of overlapping sets, pairs of closest corresponding points that are within a threshold distance of each other. The apparatus includes a transformation matrix generator configured to determine one or more transformation matrices to reduce a global distance metric that corresponds to distances between the points of each identified pair of closest corresponding points. The apparatus also includes a data set aligner configured to generate updated 3D point positions by applying the one or more transformation matrices to one or more of the multiple sets.

Abstract: Described is a system for trajectory estimation of a mobile platform, such as a UAV. In operation, the system generates an initial trajectory estimate for the mobile platform which is stored in a trajectory buffer as a buffered trajectory. Images captured at a location are compared with a location recognition database to generate a location label for a current location to designate the current location as a new location or a revisited location. If the location is a revisited location, the system determines if trajectory correction is required. If so, the buffered trajectory is corrected to generate a corrected trajectory as the drift-free trajectory. Finally, the drift-free trajectory can be used in a variety of applications. For example, the drift-free trajectory can be used to cause the mobile platform to traverse a path that coincides with the drift-free trajectory.

Abstract: A method includes generating, based on first surface data that includes three-dimensional (3D) point positions corresponding to a first portion of a surface of an object, first hole data that indicates first positions of holes in the first portion of the surface. The method includes generating, based on second surface data corresponding to a second portion of the surface of the object, second hole data that indicates second positions of the holes in the second portion of the surface. The method also includes matching the first positions to the second positions to perform an alignment with respect to the first surface data and the second surface data.

Abstract: A system and method of automated classification of rock types includes: partitioning, by a processing device, an image into partitions; extracting, by the processing device, sub-images from each of the partitions; first-level classifying, by an automated classifier, the sub-images into corresponding first classes; and second-level classifying, by the processing device, the partitions into corresponding second classes by, for each partition of the partitions, selecting a most numerous one of the corresponding first classes of the sub-images extracted from the partition. A method of displaying automated classification results on a display device is provided. The method includes: receiving, by a processing device, an image partitioned into partitions and classified into corresponding classes; and manipulating, by the processing device, the display device to display the image together with visual identification of the partitions and their corresponding classes.