Abstract: Surveillance systems and methods taught herein provide automated detection and classification of objects of interest in a submerged or underwater environment such as a body of water. The sonar systems and methods taught herein can detect and classify a variety of objects in echograms without feedback or instructions from a human operator. The sonar systems and methods taught herein include a data model that is partially trained using non-echogram image data and can associate geolocation information with detected objects of interest.

Abstract: Systems and methods described herein incorporate autonomous navigation using a vision-based guidance system. The vision-based guidance system enables autonomous trajectory planning and motion execution by the described systems and methods without feedback or communication with external operators. The systems and methods described herein can autonomously track an object of interest while seeking to obtain a diversity of views of the object of interest to aid in object identification. The systems and methods described include a robust reacquisition methodology. By handling navigation and tracking autonomously, systems described herein can react more quickly to non-cooperative moving objects of interest and can operate in situations where communications with external operators is compromised or absent.

Abstract: Surveillance systems and methods taught herein provide automated detection and classification of objects of interest in a submerged or underwater environment such as a body of water. The sonar systems and methods taught herein can detect and classify a variety of objects in echograms without feedback or instructions from a human operator. The sonar systems and methods taught herein include a data model that is partially trained using non-echogram image data and can associate geolocation information with detected objects of interest.

Abstract: A computer-implemented image processing method includes accessing image data representing a plurality of images of a plurality of vehicles and reading a data model representing a predefined vehicle attribute. The method further includes processing a portion of the image data to detect a vehicle feature of at least one of the vehicles represented in at least one of the images based on the predefined vehicle attribute, and processing the portion of the image data to generate vehicle data representing the detected vehicle feature.

Abstract: Systems and methods described herein incorporate autonomous navigation using a vision-based guidance system. The vision-based guidance system enables autonomous trajectory planning and motion execution by the described systems and methods without feedback or communication with external operators. The systems and methods described herein can autonomously track an object of interest while seeking to obtain a diversity of views of the object of interest to aid in object identification. The systems and methods described include a robust reacquisition methodology. By handling navigation and tracking autonomously, systems described herein can react more quickly to non-cooperative moving objects of interest and can operate in situations where communications with external operators is compromised or absent.

Abstract: A computer-implemented image processing method includes accessing image data representing a plurality of images of a plurality of vehicles and reading a data model representing a predefined vehicle attribute. The method further includes processing a portion of the image data to detect a vehicle feature of at least one of the vehicles represented in at least one of the images based on the predefined vehicle attribute, and processing the portion of the image data to generate vehicle data representing the detected vehicle feature.

Abstract: A computer-implemented image processing method includes accessing image data representing a plurality of images of a plurality of vehicles and reading a data model representing a predefined vehicle attribute. The method further includes processing a portion of the image data to detect a vehicle feature of at least one of the vehicles represented in at least one of the images based on the predefined vehicle attribute, and processing the portion of the image data to generate vehicle data representing the detected vehicle feature.

Abstract: A computer-implemented image processing method includes accessing image data representing a plurality of images of a plurality of vehicles and reading a data model representing a predefined vehicle attribute. The method further includes processing a portion of the image data to detect a vehicle feature of at least one of the vehicles represented in at least one of the images based on the predefined vehicle attribute, and processing the portion of the image data to generate vehicle data representing the detected vehicle feature.