Abstract: Disclosed are methods, devices, and computer-readable media for detecting lanes and objects in image frames of a monocular camera. In one embodiment, a method is disclosed comprising receiving a sample set of image frames; detecting a plurality of markers in the sample set of image frames using a convolutional neural network (CNN); fitting lines based on the plurality of markers; detecting a plurality of vanishing points based on the lines; identifying a best fitting horizon for the sample set of image frames via a RANSAC algorithm; computing an inverse perspective mapping (IPM) based on the best fitting horizon; and computing a lane width estimate based on the sample set of image frames using the IPM in a rectified view and the parallel line fitting.

Abstract: Disclosed are methods, devices, and computer-readable media for detecting lanes and objects in image frames of a monocular camera. In one embodiment, a method is disclosed comprising receiving a sample set of image frames; detecting a plurality of markers in the sample set of image frames using a convolutional neural network (CNN); fitting lines based on the plurality of markers; detecting a plurality of vanishing points based on the lines; identifying a best fitting horizon for the sample set of image frames via a RANSAC algorithm; computing an inverse perspective mapping (IPM) based on the best fitting horizon; and computing a lane width estimate based on the sample set of image frames using the IPM in a rectified view and the parallel line fitting.

Abstract: Disclosed are methods, devices, and computer-readable media for detecting lanes and objects in image frames of a monocular camera. In one embodiment, a method is disclosed comprising receiving a sample set of image frames; detecting a plurality of markers in the sample set of image frames using a convolutional neural network (CNN); fitting lines based on the plurality of markers; detecting a plurality of vanishing points based on the lines; identifying a best fitting horizon for the sample set of image frames via a RANSAC algorithm; computing an inverse perspective mapping (IPM) based on the best fitting horizon; and computing a lane width estimate based on the sample set of image frames using the IPM in a rectified view and the parallel line fitting.

Abstract: The present disclosure relates generating a geofence based on messages received periodically from a plurality of vehicles. A candidate geofence is approximated based on vehicle stoppage data obtained from a datalog of messages. For a given candidate geofence, additional stoppage data is obtained to identify additional vehicle stoppages. The locations of these additional vehicle stoppages are binned to a corresponding geospatial tile. A relatively large cluster of contiguous geospatial tiles form a geofence that may be transmitted to a client device.

Abstract: Systems and methods are disclosed for dynamic augmentation of images and videos. User input and image processing is used to both manually and automatically identify spots in images and videos where content can be convincingly inserted to form native advertisements. Ad servers can identify targeted ads for a particular viewer and automatically supply those ads for insertion in the identified spots when the viewer views the content, thus providing a low-impact targeted advertising experience. For new episodes of an existing show or series, identified spots in the known episodes can be used to identify spots in new episodes, even automatically.

Abstract: Methods and apparatus for collaborative digital imaging are disclosed. A method for digital imaging may include receiving a first image and a second image; aligning the first image with the second image; determining a first point of interest within the first image; determining a second point of interest within the second image; identifying a border for the aligned images; and creating a composite image from the aligned first and second images and the border. The composite image may include a first region of the first aligned image that includes the first point of interest and a second region of the second image that includes the second point of interest.