Abstract: A number of roadway sensing systems are described herein. An example of such is an apparatus to detect and/or track objects at a roadway with a plurality of sensors. The plurality of sensors can include a first sensor that is a radar sensor having a first field of view that is positionable at the roadway and a second sensor that is a machine vision sensor having a second field of view that is positionable at the roadway, where the first and second fields of view at least partially overlap in a common field of view over a portion of the roadway. The example system includes a controller configured to combine sensor data streams for at least a portion of the common field of view from the first and second sensors to detect and/or track the objects.

Abstract: A number of roadway sensing systems are described herein. An example of such is an apparatus to detect and/or track objects at a roadway with a plurality of sensors. The plurality of sensors can include a first sensor that is a radar sensor having a first field of view that is positionable at the roadway and a second sensor that is a machine vision sensor having a second field of view that is positionable at the roadway, where the first and second fields of view at least partially overlap in a common field of view over a portion of the roadway. The example system includes a controller configured to combine sensor data streams for at least a portion of the common field of view from the first and second sensors to detect and/or track the objects.

Abstract: A number of roadway sensing systems are described herein. An example of such is an apparatus to detect and/or track objects at a roadway with a plurality of sensors. The plurality of sensors can include a first sensor that is a radar sensor having a first field of view that is positionable at the roadway and a second sensor that is a machine vision sensor having a second field of view that is positionable at the roadway, where the first and second fields of view at least partially overlap in a common field of view over a portion of the roadway. The example system includes a controller configured to combine sensor data streams for at least a portion of the common field of view from the first and second sensors to detect and/or track the objects.

Abstract: A number of roadway sensing systems are described herein. An example of such is an apparatus to detect and/or track objects at a roadway with a plurality of sensors. The plurality of sensors can include a first sensor that is a radar sensor having a first field of view that is positionable at the roadway and a second sensor that is a machine vision sensor having a second field of view that is positionable at the roadway, where the first and second fields of view at least partially overlap in a common field of view over a portion of the roadway. The example system includes a controller configured to combine sensor data streams for at least a portion of the common field of view from the first and second sensors to detect and/or track the objects.

Abstract: A number of roadway sensing systems are described herein. An example of such is an apparatus to detect and/or track objects at a roadway with a plurality of sensors. The plurality of sensors can include a first sensor that is a radar sensor having a first field of view that is positionable at the roadway and a second sensor that is a machine vision sensor having a second field of view that is positionable at the roadway, where the first and second fields of view at least partially overlap in a common field of view over a portion of the roadway. The example system includes a controller configured to combine sensor data streams for at least a portion of the common field of view from the first and second sensors to detect and/or track the objects.

Abstract: A number of roadway sensing systems are described herein. An example of such is an apparatus to detect and/or track objects at a roadway with a plurality of sensors. The plurality of sensors can include a first sensor that is a radar sensor having a first field of view that is positionable at the roadway and a second sensor that is a machine vision sensor having a second field of view that is positionable at the roadway, where the first and second fields of view at least partially overlap in a common field of view over a portion of the roadway. The example system includes a controller configured to combine sensor data streams for at least a portion of the common field of view from the first and second sensors to detect and/or track the objects.

Abstract: The present invention relates generally to thermal imaging systems and methods and uses therefor, and in a particular though non-limiting embodiment, to a method of detecting corrosion under insulation, as well as corrosion-related material data associated therewith. The system utilizes advanced infrared imaging video cameras to detect characteristic signatures of wet thermal traits on process equipment. Various embodiments of the invention integrate equipment, automation, and algorithms to form a method for identifying wet thermal insulation by scanning multiple locations along insulated piping, tanks, or other manufacturing equipment. Such scans can occur individually, sequentially, or simultaneously, with the results then being stored and comparatively analyzed.

Abstract: A system and method are provided for associating faces to determine whether the faces are similar. For example a captured face may be associated with known faces to determine which known face the captured most closely resembles. The system and method incorporate the use of Gabor filters applied to masked face images to develop covariance matrixes. The covariance matrixes are subject to a similarity measure to determine the similarity of the images.

Abstract: A system having a layered architecture for constructing dynamic social network from image data of actors and events. It may have a low layer for capturing raw data and identifying actors and events. The system may have a middle layer that receives actor and event information from the low layer and puts it in to a two dimensional matrix. A high layer of the system may add weighted relationship information to the matrix to form the basis for constructing a social network. The system may have a sliding window thus making the social network dynamic.

Abstract: A system for social network construction. Video analytics and association may be used to develop a social network. Also, social groups may be developed from temporal proximity of persons. In the case of several social networks, they may be collapsed into one network or a weighted graph that mining algorithms can handle.

Abstract: A method to geo-reference a target between subsystems of a targeting system is provided. The method includes receiving a target image formed at a sender subsystem location, generating target descriptors for a first selected portion of the target image, sending target location information and the target descriptors from a sender subsystem of the targeting system to a receiver subsystem of the targeting system, pointing an optical axis of a camera of the receiver subsystem at the target based on the target location information received from the sending subsystem, forming a target image at a receiver subsystem location when the optical axis is pointed at the target, and identifying a second selected portion of the target image formed at the receiver subsystem location that is correlated to the first selected portion of the target image formed at the sender subsystem location.

Abstract: A seed search of a subset of analytical data corresponding to video objects displayable in a plurality of video frames is carried out to identify video objects that most closely match a selected video object and then complete searches of the analytical data may be carried out so as to identify video objects that most closely match each video object identified during the seed search. The video objects having the greatest number of occurrences of being identified during the complete searches may be displayed by a graphical user interface (GUI). In this way, the GUI may display the video objects in an order based on how closely each video object matches the selected video object and/or a video object identified during the seed search, which may an order different than an order based on a time when each video object was captured.

Abstract: A method is provided for displaying symbology on a see-through display device in an environment with at least one real-world object. The method includes selecting the at least one real-world object; selecting symbology to display with the at least one real-world object; and conformally displaying the symbology with the at least one real-world object.

Abstract: The present invention relates generally to thermal imaging systems and methods and uses therefor, and in a particular though non-limiting embodiment, to a method of detecting corrosion under insulation, as well as corrosion-related material data associated therewith. The system utilizes advanced infrared imaging video cameras to detect characteristic signatures of wet thermal traits on process equipment. Various embodiments of the invention integrate equipment, automation, and algorithms to form a method for identifying wet thermal insulation by scanning multiple locations along insulated piping, tanks, or other manufacturing equipment. Such scans can occur individually, sequentially, or simultaneously, with the results then being stored and comparatively analyzed.

Abstract: A method to geo-reference a target between subsystems of a targeting system is provided. The method includes receiving a target image formed at a sender subsystem location, generating target descriptors for a first selected portion of the target image, sending target location information and the target descriptors from a sender subsystem of the targeting system to a receiver subsystem of the targeting system, pointing an optical axis of a camera of the receiver subsystem at the target based on the target location information received from the sending subsystem, forming a target image at a receiver subsystem location when the optical axis is pointed at the target, and identifying a second selected portion of the target image formed at the receiver subsystem location that is correlated to the first selected portion of the target image formed at the sender subsystem location.

Abstract: A system for constructing a social network structure from image data. A number of image sensors or cameras may be placed in various areas of a large facility having much throughput of many people, such as an airport. Images from the cameras may indicate an event. The event may be activity recognized by video analytics of the system. Video analytics may detect, track and associate people, faces and objects in the images. The analytics may provide activity recognition from images. A dynamic social network may be constructed from information provided by the video analytics. Analysis of a constructed social network may reveal further information which can be available for improving dynamic social network construction.

Abstract: A system for social network construction. Video analytics and association may be used to develop a social network. Also, social groups may be developed from temporal proximity of persons. In the case of several social networks, they may be collapsed into one network or a weighted graph that mining algorithms can handle.

Abstract: A system having a layered architecture for constructing dynamic social network from image data of actors and events. It may have a low layer for capturing raw data and identifying actors and events. The system may have a middle layer that receives actor and event information from the low layer and puts it in to a two dimensional matrix. A high layer of the system may add weighted relationship information to the matrix to form the basis for constructing a social network. The system may have a sliding window thus making the social network dynamic.

Abstract: A system and method are provided for associating faces to determine whether the faces are similar. For example a captured face may be associated with known faces to determine which known face the captured most closely resembles. The system and method incorporate the use of Gabor filters applied to masked face images to develop covariance matrixes. The covariance matrixes are subject to a similarity measure to determine the similarity of the images.

Abstract: A seed search of a subset of analytical data corresponding to video objects displayable in a plurality of video frames is carried out to identify video objects that most closely match a selected video object and then complete searches of the analytical data may be carried out so as to identify video objects that most closely match each video object identified during the seed search. The video objects having the greatest number of occurrences of being identified during the complete searches may be displayed by a graphical user interface (GUI). In this way, the GUI may display the video objects in an order based on how closely each video object matches the selected video object and/or a video object identified during the seed search, which may an order different than an order based on a time when each video object was captured.