Abstract: A system for mapping traffic lights and for determining traffic light relevancy for use in autonomous vehicle navigation. The system may include at least one processor programmed to: receive at least one location identifier associated with a traffic light; receive a state identifier associated with the traffic light; receive navigational information indicative of one or more aspects of motion of the first vehicle along the road segment, and determine, based on the navigational information, a lane of travel traversed by the first vehicle along the road segment. The processor may also determine whether the traffic light is relevant to the lane of travel traversed by the first vehicle; update an autonomous vehicle road navigation model relative to the road segment; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.

Abstract: Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.

Abstract: Identifying a local coordinate system is described for gesture recognition. In one example, a method includes receiving a gesture from a user across a horizontal axis at a depth camera, determining a horizontal vector for the user based on the received user gesture, determining a vertical vector; and determining a rotation matrix to convert positions of user gestures received by the camera to a frame of reference of the user.

Abstract: A system for mapping traffic lights and for determining traffic light relevancy for use in autonomous vehicle navigation. The system may include at least one processor programmed to: receive at least one location identifier associated with a traffic light; receive a state identifier associated with the traffic light; receive navigational information indicative of one or more aspects of motion of the first vehicle along the road segment, and determine, based on the navigational information, a lane of travel traversed by the first vehicle along the road segment. The processor may also determine whether the traffic light is relevant to the lane of travel traversed by the first vehicle; update an autonomous vehicle road navigation model relative to the road segment; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.

Abstract: Identifying a local coordinate system is described for gesture recognition. In one example, a method includes receiving a gesture from a user across a horizontal axis at a depth camera, determining a horizontal vector for the user based on the received user gesture, determining a vertical vector; and determining a rotation matrix to convert positions of user gestures received by the camera to a frame of reference of the user.

Abstract: Identifying a local coordinate system is described for gesture recognition. In one example, a method includes receiving a gesture from a user across a horizontal axis at a depth camera, determining a horizontal vector for the user based on the received user gesture, determining a vertical vector; and determining a rotation matrix to convert positions of user gestures received by the camera to a frame of reference of the user.

Abstract: Systems, apparatuses and methods may track air gestures within a bounding box in a field of view (FOV) of a camera. Air gestures made within the bounding box may be translated and mapped to a display screen. Should the hand, or other member making the air gesture, exit the bounds of the bounding box, the box will be dragged along by the member to a new location within the FOV.

Abstract: Systems, apparatuses and/or methods may provide for generating a packing order of items within a container that consolidates the items into a reduced space. Items may be scanned with a three-dimensional (3D) imager, and models may be generated of the items based on the data from the 3D imager. The items may be located within minimal-volume enclosing bounding boxes, which may be analyzed to determine whether they may be merged together in one of their bounding boxes, or into a new bounding box that is spatially advantageous in terms of packing. If a combination of items is realizable and is determined to take up less space in a bounding box than the bounding boxes of the items considered separately, then they may be merged into a single bounding box. Thus, a spatially efficient packing sequence for a plurality of real objects may be generated to maximize packing efficiency.

Abstract: A system for hand gesture recognition includes a display, camera, memory, and processor. When the processor is to execute instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor may also estimate an average motion vector of the object, determine a first histogram of optical flow (HOOF) based on the one or more motion vectors and the average motion vector, determine depth values based on motion vectors from the first HOOF, and determine a second histogram of optical flow (HOOF) based on the depth values. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors, a shape descriptor, and the average motion vector and classify the descriptor as a gesture.

Abstract: A system for hand gesture recognition is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors and the average motion vector and classify the descriptor as a gesture.

Abstract: A system for a tracker for cursor navigation is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to extract an object mask of an object and execute an optical flow on good feature points from the object mask. The processor is also to estimate a movement of the object and render a cursor on the display based on the movement of the object.

Abstract: In accordance with some embodiments, connected-component labeling is performed in both the screen dimensions (which may be referred to as the x and y dimensions) and a depth dimension to label objects in a depth image. Then the contour of labeled blobs may be used to identify an object in the depth image. Using contours may be advantageous in some embodiments because it reduces the amount of data that must be handled and the extent of computations, compared to conventional techniques which use bit map based operations.

Abstract: Systems, apparatuses and methods may track air gestures within a bounding box in a field of view (FOV) of a camera. Air gestures made within the bounding box may be translated and mapped to a display screen. Should the hand, or other member making the air gesture, exit the bounds of the bounding box, the box will be dragged along by the member to a new location within the FOV.

Abstract: A head-mounted display (HMD) device to be worn by a user in a physical environment (PE) is controlled. A 3D virtual environment (VE) is modeled to include a virtual controllable object subject to virtual control input. Motion of the position, head, and hands of the user is monitored in the PE, and a physical surface in the PE is detected. A virtual user interface (vUI) is placed in the VE relative to a virtual perspective of the user. The vUI includes an information display and at least one virtual touch control to produce the virtual control input in response to virtual manipulation of the virtual touch control. The vUI's placement is determined to coincide with the physical surface in the PE relative to the position of the user in the PE.

Abstract: A system for a tracker for cursor navigation is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to extract an object mask of an object and execute an optical flow on good feature points from the object mask. The processor is also to estimate a movement of the object and render a cursor on the display based on the movement of the object.

Abstract: A system for hand gesture recognition includes a display, camera, memory, and processor. When the processor is to execute instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor may also estimate an average motion vector of the object, determine a first histogram of optical flow (HOOF) based on the one or more motion vectors and the average motion vector, determine depth values based on motion vectors from the first HOOF, and determine a second histogram of optical flow (HOOF) based on the depth values. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors, a shape descriptor, and the average motion vector and classify the descriptor as a gesture.

Abstract: A system for a tracker for cursor navigation is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to extract an object mask of an object and execute an optical flow on good feature points from the object mask. The processor is also to estimate a movement of the object and render a cursor on the display based on the movement of the object.

Abstract: Identifying a local coordinate system is described for gesture recognition. In one example, a method includes receiving a gesture from a user across a horizontal axis at a depth camera, determining a horizontal vector for the user based on the received user gesture, determining a vertical vector; and determining a rotation matrix to convert positions of user gestures received by the camera to a frame of reference of the user.

Abstract: A system for hand gesture recognition is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors and the average motion vector and classify the descriptor as a gesture.

Abstract: A system for a tracker for cursor navigation is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to extract an object mask of an object and execute an optical flow on good feature points from the object mask. The processor is also to estimate a movement of the object and render a cursor on the display based on the movement of the object.