Abstract:
Techniques and systems are provided for prioritizing objects for object recognition in one or more video frames. For example, a current video frame is obtained, and a objects are detected in the current video frame. State information associated with the objects is determined. Priorities for the objects can also be determined. For example, a priority can be determined for an object based on state information associated with the object. Object recognition is performed for at least one object from the objects based on priorities determined for the at least one object. For instance, object recognition can be performed for objects having higher priorities before objects having lower priorities.
Abstract:
Certain aspects of the present disclosure provide methods and apparatus for operating a wearable display device. Certain aspects of the present disclosure provide a method for operating a wearable display device. The method includes determining a position of the wearable display device based on a motion sensor. The method includes rendering, by a graphics processing unit, an image based on the determined position. The method includes determining a first updated position of the wearable display device based on the motion sensor. The method includes warping, by a warp engine, a first portion of the rendered image based on the first updated position. The method includes displaying the warped first portion of the rendered image on a display of the wearable display device.
Abstract:
An apparatus includes a first sensor configured to generate first sensor data. The first sensor data is related to an occupant of a vehicle. The apparatus further includes a depth sensor and a processor. The depth sensor is configured to generate data corresponding to a volume associated with at least a portion of the occupant. The processor is configured to receive the first sensor data and to activate the depth sensor based on the first sensor data.
Abstract:
Techniques and systems are provided for processing video data. For example, techniques and systems are provided for performing context-aware object or blob tracker updates (e.g., by updating a motion model of a blob tracker). In some cases, to perform a context-aware blob tracker update, a blob tracker is associated with a first blob. The first blob includes pixels of at least a portion of one or more foreground objects in one or more video frames. A split of the first blob and a second blob in a current video frame can be detected, and a motion model of the blob tracker is reset in response to detecting the split of the first blob and the second blob. In some cases, a motion model of a blob tracker associated with a merged blob is updated to include a predicted location of the blob tracker in a next video frame. The motion model can be updated by using a previously predicted location of blob tracker as the predicted location of the blob tracker in the next video frame in response to the blob tracker being associated with the merged blob. The previously predicted location of the blob tracker can be determined using a blob location of a blob from a previous video frame.
Abstract:
Examples are described for overlaying primitives, arranged as concentric circles, in circular images onto respective mesh models to generate rectangular images representative of a 360-degree video or image. Portions of the rectangular images are blended to generate a stitched rectangular image, and image content for display is generated based on the stitched rectangular image.
Abstract:
Techniques and systems are provided for encoding video data. For example, a method of encoding video data includes obtaining a background picture that is generated based on a plurality of pictures captured by an image sensor. The background picture is generated to include background portions identified in each of the captured pictures. The method further includes encoding, into a video bitstream, a group of pictures captured by the image sensor. The group of pictures includes at least one random access picture. Encoding the group of pictures includes encoding at least a portion of the at least one random access picture using inter-prediction based on the background picture.
Abstract:
A system and method of object detection are disclosed. In a particular implementation, a method of processing an image includes receiving, at a processor, image data associated with an image of a scene. The scene includes a road region. The method further includes detecting the road region based on the image data and determining a subset of the image data. The subset excludes at least a portion of the image data corresponding to the road region. The method further includes performing an object detection operation on the subset of the image data to detect an object. The object detection operation performed on the subset of the image data is exclusive of the at least a portion of the image data corresponding to the road region.
Abstract:
Disclosed is a method and apparatus for implementing a virtual mouse. In one embodiment, the functions implemented include activating the virtual mouse, determining a location of a cursor icon associated with the virtual mouse, and deactivating the virtual mouse. In various embodiments, the position of virtual mouse is determined by a processor based upon an orientation or position of a finger touching a touchscreen and a measured or calculated pressure applied by the finger to the touchscreen.
Abstract:
A method for three-dimensional face generation is described. An inverse depth map is calculated based on a depth map and an inverted first matrix. The inverted first matrix is generated from two images in which pixels are aligned vertically and differ horizontally. The inverse depth map is normalized to correct for distortions in the depth map caused by image rectification. A three-dimensional face model is generated based on the inverse depth map and one of the two images.
Abstract:
A method for picture processing is described. A first tracking area is obtained. A second tracking area is also obtained. The method includes beginning to track the first tracking area and the second tracking area. Picture processing is performed once a portion of the first tracking area overlapping the second tracking area passes a threshold.