Abstract: A system is configured to label computer vision datasets using eye tracking of users that track objects depicted in imagery to label the datasets. The imagery may include moving images (e.g., video) or still images. By using eye tracking, users may be able to label large amounts of imagery more efficiently than by manually labeling datasets using conventional input devices. A user may be instructed to watch a particular object during a playback of the video while an imaging device determines a direction of the user's gaze which correlates with a location in the imagery. An application may then associate the location in the imagery determined from the user's gaze as a location of the object on a frame-by-frame basis, or for certain frames.

Abstract: In some embodiments, a first image may be captured from a first field of view using a first exposure time. A second image may be captured from a second field of view using a second exposure time that is different than the first exposure time. An overlapping field of view may be defined by an overlapping portion of the first field of view and the second field of view. Histograms may be created for the first image and the second image, and possibly more images that include different exposure times and represent the overlapping field of view. The histograms may be analyzed to determine a presence or an absence of a light source in the overlapping field of view.

Abstract: A system is configured to label computer vision datasets using eye tracking of users that track objects depicted in imagery to label the datasets. The imagery may include moving images (e.g., video) or still images. By using eye tracking, users may be able to label large amounts of imagery more efficiently than by manually labeling datasets using conventional input devices. A user may be instructed to watch a particular object during a playback of the video while an imaging device determines a direction of the user's gaze which correlates with a location in the imagery. An application may then associate the location in the imagery determined from the user's gaze as a location of the object on a frame-by-frame basis, or for certain frames.

Abstract: This disclosure describes systems, methods, and apparatus for automating the verification of aerial vehicle sensors as part of a pre-flight, flight departure, in-transit flight, and/or delivery destination calibration verification process. At different stages, aerial vehicle sensors may obtain sensor measurements about objects within an environment, the obtained measurements may be processed to determine information about the object, as presented in the measurements, and the processed information may be compared with the actual information about the object to determine a variation or difference between the information. If the variation is within a tolerance range, the sensor may be auto adjusted and operation of the aerial vehicle may continue. If the variation exceeds a correction range, flight of the aerial vehicle may be aborted and the aerial vehicle routed for a full sensor calibration.

Abstract: This disclosure describes systems, methods, and apparatus for automating the verification of aerial vehicle sensors as part of a pre-flight, flight departure, in-transit flight, and/or delivery destination calibration verification process. At different stages, aerial vehicle sensors may obtain sensor measurements about objects within an environment, the obtained measurements may be processed to determine information about the object, as presented in the measurements, and the processed information may be compared with the actual information about the object to determine a variation or difference between the information. If the variation is within a tolerance range, the sensor may be auto adjusted and operation of the aerial vehicle may continue. If the variation exceeds a correction range, flight of the aerial vehicle may be aborted and the aerial vehicle routed for a full sensor calibration.