Apple Inc.

Mobileye Vision Technologies Ltd.

Primesense Ltd

Amiad Gurman

Gideon Stein

Tomer Yanir

Dan Rosenbaum

Eran Guendelman

Michael Brand

David Aloni

Yonatan Samet

Oren Tropp

Erez Sali

Gideon P Stein

A method is provided for preventing a collision between a motor vehicle and a pedestrian. The method uses a camera and a processor mountable in the motor vehicle. A candidate image is detected. Based on a change of scale of the candidate image, it may be determined that the motor vehicle and the pedestrian are expected to collide, thereby producing a potential collision warning. Further information from the image frames may be used to validate the potential collision warning. The validation may include an analysis of the optical flow of the candidate image, that lane markings prediction of a straight road, a calculation of the lateral motion of the pedestrian, if the pedestrian is crossing a lane mark or curb and/or if the vehicle is changing lanes.

Pedestrian collision warning system

A method for processing data includes receiving a depth map of a scene containing at least an upper body of a humanoid form. The depth map is processed so as to identify a head and at least one arm of the humanoid form in the depth map. Based on the identified head and at least one arm, and without reference to a lower body of the humanoid form, an upper-body pose, including at least three-dimensional (3D) coordinates of shoulder joints of the humanoid form, is extracted from the depth map.

Upper-body skeleton extraction from depth maps

A method for depth mapping includes receiving an image of a pattern of spots that has been projected onto a scene, which includes a hand having fingers. The image is processed in order to segment and find a three-dimensional (3D) location of the hand. Based on the spots appearing on the hand in the 3D location, a first depth value that is characteristic of the hand and a second depth value that is characteristic of a background of the scene behind the hand are computed. The spots in a vicinity of the hand in the image between the first and second depth values are sorted in order to extract separate, respective contours of each of the fingers. The respective contours are processed in order to identify a posture of the hand and fingers.

Depth mapping with enhanced resolution

A method for depth mapping includes receiving an image of a pattern of spots that has been projected onto a scene, which includes a feature having dimensions that are less than twice an average distance between the spots in the pattern that is projected onto the feature. The image is processed so as to find a 3D location of the feature by computing three-dimensional (3D) coordinates of points on the feature based on transverse shifts of the spots in the image. The spots appearing on the feature in the 3D location are connected in order to find depth coordinates of the points on the feature with a resolution finer than a depth increment corresponding to a transverse shift equal to the average distance between the spots in the image.

A method for providing a forward collision warning using a camera mountable in a motor vehicle. The method acquires multiple image frames at known time intervals. A patch may be selected in at least one of the image frames. Optical flow may be tracked between the image frames of multiple image points of the patch. Based on the fit of the image points to a model, a time-to collision (TTC) may be determined if a collision is expected. The image points may be fit to a road surface model and a portion of the image points is modeled to be imaged from a road surface. A collision is not expected based on the fir of the image points to the road surface model.

Forward collision warning trap and pedestrian advanced warning system

A method for processing data includes receiving a temporal sequence of depth maps of a scene containing a humanoid form having a head. The depth maps include a matrix of pixels having respective pixel depth values. A digital processor processes at least one of the depth maps so as to find a location of the head and estimates dimensions of the humanoid form based on the location. The processor tracks movements of the humanoid form over the sequence using the estimated dimensions.

Extraction of skeletons from 3D maps

A method for processing data includes receiving a depth map of a scene containing a humanoid form. The depth map is processed so as to identify three-dimensional (3D) connected components in the scene, each connected component including a set of the pixels that are mutually adjacent and have mutually-adjacent depth values. Separate, first and second connected components are identified as both belonging to the humanoid form, and a representation of the humanoid form is generated including both of the first and second connected components.

Analysis of three-dimensional scenes

A method for depth mapping includes receiving an image of a pattern of spots that has been projected onto a scene, which includes a feature having a set of elongate appendages, which have respective transverse dimensions that are less than twice an average distance between the spots in the pattern that is projected onto the feature. The image is processed in order to segment and find a three-dimensional (3D) location of the feature. The spots appearing on the feature in the 3D location are connected in order to extract separate, respective contours of the appendages.

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Elkana, Israel

Amiad Gurman