Abstract: Described herein are systems and methods for locating an object detected in a video. The system detects a bounding box at least partially around an object in a first frame at a first time in the video and a second frame in the video corresponding to a second time. The system determines whether there is no motion within the bounding box of the second frame. The system compares edge information, or color information, or intensity information associated with one or more pixels in the first frame, to edge information, or color information, or intensity information associated with one or more pixels within the bounding box. The system generates a score based on the comparison. The system further determines based on the score if the object is present in the second frame. The system also determines an estimated timeframe window of a first appearance of the object.

Abstract: Systems and methods are provided for selecting an optimized data model architecture subject to resource constraints. One or more resource constraints for target deployment are identified, and random model architectures are generated from a set of model architecture production rules subject to the one or more resource constraints. Each random model architecture is defined by randomly chosen values for one or more meta parameters and one or more layer parameters. One or more of the random model architectures are adaptively refined to improve performance relative to a metric, and the refined model architecture with the best performance relative to the metric is selected.

Abstract: Described herein are systems and methods for locating an object detected in a video. The system detects a bounding box at least partially around an object in a first frame at a first time in the video and a second frame in the video corresponding to a second time. The system determines whether there is no motion within the bounding box of the second frame. The system compares edge information, or color information, or intensity information associated with one or more pixels in the first frame, to edge information, or color information, or intensity information associated with one or more pixels within the bounding box. The system generates a score based on the comparison. The system further determines based on the score if the object is present in the second frame. The system also determines an estimated timeframe window of a first appearance of the object.

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: 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: Systems and methods are provided for selecting an optimized data model architecture subject to resource constraints. One or more resource constraints for target deployment are identified, and random model architectures are generated from a set of model architecture production rules subject to the one or more resource constraints. Each random model architecture is defined by randomly chosen values for one or more meta parameters and one or more layer parameters. One or more of the random model architectures are adaptively refined to improve performance relative to a metric, and the refined model architecture with the best performance relative to the metric is selected.

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.