Abstract: A hitch assistance system for assisting in maneuvering a vehicle toward a trailer includes a sensing system that includes an image sensor and a controller. The controller is configured to: identify a position of a coupler of the trailer based on image data received from the image sensor by utilizing a coupler detection model trained based on a library of images depicting couplers; control movement of the vehicle toward an aligned position based on the identified position of the coupler, wherein a hitch ball of the vehicle is aligned with the coupler of the trailer; determine a prospective location of the coupler in image data based on sensor data received from the sensing system; and crop image data based on the determined prospective location of the coupler.

Abstract: Disclosed herein are systems, methods, and computer program products for predicting movement of an object in a real-world environment. The methods comprise: obtaining a plurality of image frames captured in a sequence during a period of time; identifying first image frames of the plurality of image frames that contain an image of at least one object with one or more turn signals; analyzing the first image frames to obtain a classification for a pose of the at least one object; using the classification of the pose of the at least one object to further obtain a type classification for at least one of the turn signals and a state classification for a state of at least one of the turn signals; and predicting movement of the at least one object based at least on the type and state classifications obtained for at least one of the turn signals.

Abstract: A system uses data captured by vehicle-mounted sensors to generate a view of a ground surface. The system does this by receiving digital image frames and associating a location and pose of the vehicle that captured the image with each digital image frame. The system will access a three dimensional (3D) ground surface estimation model of the ground surface, select a region of interest (ROI) of the ground surface, and select a vehicle pose. The system will identify digital image frames that are associated with the pose and also with a location that corresponds to the ROI. The system will generate a visual representation of the ground surface in the ROI by projecting ground data for the ROI from the ground surface estimation model to normalized 2D images that are created from the digital image frames. The system will save the visual representation to a two-dimensional grid.

Abstract: Disclosed herein are systems, methods, and computer program products for predicting movement of an object in a real-world environment. The methods comprise: obtaining a plurality of image frames captured in a sequence during a period of time; identifying first image frames of the plurality of image frames that contain an image of at least one object with one or more turn signals; analyzing the first image frames to obtain a classification for a pose of the at least one object; using the classification of the pose of the at least one object to further obtain a type classification for at least one of the turn signals and a state classification for a state of at least one of the turn signals; and predicting movement of the at least one object based at least on the type and state classifications obtained for at least one of the turn signals.

Abstract: A system uses video of a vehicle to detect and classify the vehicle's pose. The system generates an image stack by scaling and shifting a set of digital image frames from the video to a fixed scale, yielding a sequence of images over a time period. The system processes the image stack with a classifier to determine the pose of the object. The system also may determine state and class of visible turn signals on the object, as well as predict the vehicle's direction of travel.

Abstract: A system uses video of a vehicle to detect and classify the vehicle's pose. The system generates an image stack by scaling and shifting a set of digital image frames from the video to a fixed scale, yielding a sequence of images over a time period. The system processes the image stack with a classifier to determine the pose of the object. The system also may determine state and class of visible turn signals on the object, as well as predict the vehicle's direction of travel.

Abstract: A system uses data captured by vehicle-mounted sensors to generate a view of a ground surface. The system does this by receiving digital image frames and associating a location and pose of the vehicle that captured the image with each digital image frame. The system will access a three dimensional (3D) ground surface estimation model of the ground surface, select a region of interest (ROI) of the ground surface, and select a vehicle pose. The system will identify digital image frames that are associated with the pose and also with a location that corresponds to the ROI. The system will generate a visual representation of the ground surface in the ROI by projecting ground data for the ROI from the ground surface estimation model to normalized 2D images that are created from the digital image frames. The system will save the visual representation to a two-dimensional grid.

Abstract: A system uses video of a vehicle or other object to detect and classify an active turn sign on the object. The system generates an image stack by scaling and shifting a set of digital image frames from the video to a fixed scale, yielding a sequence of images over a time period. The system processes the image stack with a classifier to determine a pose of the object, as well as the state and class of each visible turn signals on the object. When the system determines that a turn signal is active, the system will predict an action that the object will take based on the class of that signal.

Abstract: A system uses video of a vehicle or other object to detect and classify an active turn sign on the object. The system generates an image stack by scaling and shifting a set of digital image frames from the video to a fixed scale, yielding a sequence of images over a time period. The system processes the image stack with a classifier to determine a pose of the object, as well as the state and class of each visible turn signals on the object. When the system determines that a turn signal is active, the system will predict an action that the object will take based on the class of that signal.

Abstract: The present disclosure provides a computer implemented method of image processing comprising, upon receiving of first and second images from an imaging unit, the first and second images being respectively associated with first and second rotational changes between a reference orientation and the orientations of the first and second images: processing data representative of the first image and of the second image to compensate the first and second rotational changes between the reference orientation and the respective orientations of the first and second images, thereby obtaining first and second corrected images; processing the first corrected image to detect distinctive keypoints within a fronto-parallel strip of the first corrected image; searching keypoints in the second corrected image corresponding to the detected keypoints, and estimating a geometric transformation between the first and second images based on matching the keypoints in the first and the second corrected images.

Abstract: A system and method for fraud detection may include (a) receiving, by a model based fraud detection unit, a plurality of images related to fraud attempts, (b) determining, by the unit, for each of the images, whether or not the image is related to a fraud attempt, (c) if a percentage of the images identified as related to fraud attempts is lower than a threshold, then automatically modifying at least one coefficient in the model, and repeating steps (a), (b) and (c) until a percentage of the images identified as related to fraud attempts is higher than the threshold.

Abstract: Images are processed to compensate for rolling shutter effects. A pair of images are registered. A set of pixel rows in the first image and a corresponding set of pixel rows in the second image are obtained. A parametric model is generated characterizing a transformation between pixels in the set of pixel rows in the first image with pixels in the corresponding set of pixel rows of the second image. Using the generated parametric model, the set of pixel rows in the second image is warped with respect to the set of pixel rows in the first image, reducing rolling shutter effects.

Abstract: The present disclosure provides a computer implemented method of image processing comprising, upon receiving of first and second images from an imaging unit, the first and second images being respectively associated with first and second rotational changes between a reference orientation and the orientations of the first and second images: processing data representative of the first image and of the second image to compensate the first and second rotational changes between the reference orientation and the respective orientations of the first and second images, thereby obtaining first and second corrected images; processing the first corrected image to detect distinctive keypoints within a fronto-parallel strip of the first corrected image; searching keypoints in the second corrected image corresponding to the detected keypoints, and estimating a geometric transformation between the first and second images based on matching the keypoints in the first and the second corrected images.

Abstract: Generating 3D representations of a scene represented by a first video stream captured by video cameras. Identifying a transition between cameras, retrieving parameters of a first set of viewing configurations, providing 3D video representations representing the scene at several sets of viewing configurations different from the first set of viewing configurations, and generating an integrated video stream enabling 3D display of the scene by integration of at least two video streams having respective sets of viewing configurations, which are mutually different. Another provided process is for synthesizing an image of an object from a first image, captured by a certain camera at a first viewing configuration. Assigning a 3D model to a portion of a segmented object, calculating a modified image of the portion of the object from a viewing configuration different from the first viewing configuration, and embedding the modified image in a frame for stereoscopy.