Eyewear providing an interactive augmented reality experience between two eyewear devices by using alignment between respective 6DOF trajectories, also referred to herein as ego motion alignment. An eyewear device of user A and an eyewear device of user B track the eyewear device of the other user, or an object of the other user, such as on the user's face, to provide the collaborative AR experience. This enables sharing common three-dimensional content between multiple eyewear users without using or aligning the eyewear devices to common image content such as a marker, which is a more lightweight solution with reduced computational burden on a processor. An inertial measurement unit may also be used to align the eyewear devices.

An anonymization apparatus 6 is proposed for the generation of anonymized images 9, wherein surveillance images 5 are provided through video surveillance of a surveillance region 3 by means of at least one camera 2, with a recognition module 11, wherein the surveillance images 5 are provided to the recognition module 11, wherein the recognition module 11 is configured to recognize persons 4 contained in the surveillance images 5, with a processing module 13, wherein the processing module 13 is configured to process the surveillance images 5 into the anonymized images 9, wherein at least one person 4 or person segment included in the surveillance images 5 is anonymized in the anonymized images 9, wherein the processing module 13 is configured to replace the recognized person 4 or person segment by an animated person model 14 for the purpose of anonymization.

This application relates to a lane edge extraction method and apparatus, an autonomous driving system, a vehicle, and a storage medium. The lane edge extraction method includes the steps of: receiving tracking edge points, about lane edges, of an immediately preceding frame of an edge image sequence; determining observation edge points, about the lane edges, of a current frame of the edge image sequence; continuing and correcting the tracking edge points of the immediately preceding frame based on the observation edge points of the current frame, to obtain temporary tracking edge points of the current frame; fitting a lane edge curve based on the temporary tracking edge points; and excluding outliers from the temporary tracking edge points based on the lane edge curve, to form tracking edge points of the current frame. The method can improve the stability and accuracy of lane edge extraction.

The mesh tracking described herein involves mesh tracking on 3D face models. In contrast to existing mesh tracking algorithms which generally require user intervention and manipulation, the mesh tracking algorithm is fully automatic once a template mesh is provided. In addition, an eye and mouth boundary detection algorithm is able to better reconstruct the shape of eyes and mouths.

There is included a method and apparatus comprising computer code configured to cause a processor or processors to perform obtaining video data including at least one body part of a person, selecting keypoints of the at least one body part based on a predetermined rehabilitation category, extracting a motion feature of the at least one body part from the video data, scoring the motion feature based on the predetermined rehabilitation category, and generating a display illustrating the motion feature and said scoring of the motion feature.

Embodiments generally relate to a machine-implemented method of automatically adjusting the range of a depth data recording executed by at least one processing device. The method comprises determining, by the at least one processing device, at least one positions of a subject to be recorded; determining, by the at least one processing device, at least one spatial range based on the positions of the subject; receiving depth information; and constructing, by the at least one processing device, a depth data recording based on the received depth information limited by the at least one spatial range.

Systems and methods are presented for detecting physical contacts effectuated by actions performed by an entity participating in an event. An action, performed by the entity, is detected based on a sequence of pose data associated with the entity's performance in the event. A contact with another entity in the event is detected based on data associated with the detected action. The action and the contact detections are employed by neural-network based detectors.

Free space machine interface and control can be facilitated by predictive entities useful in interpreting a control object's position and/or motion (including objects having one or more articulating members, i.e., humans and/or animals and/or machines). Predictive entities can be driven using motion information captured using image information or the equivalents. Predictive information can be improved applying techniques for correlating with information from observations.

There is provided mechanisms for calibration of an eye tracking system. An eye tracking system comprises a pupil centre corneal reflection (PCCR) based eye tracker and a non-PCCR based eye tracker. A method comprises obtaining at least one first eye position of a subject by applying the PCCR based eye tracker on an image set depicting the subject. The method comprises calibrating a head model of the non-PCCR based eye tracker, as applied on the image set, for the subject using the obtained at least one first eye position from the PCCR based eye tracker as ground truth. The head model comprises facial features that include at least one second eye position. The calibrating involves positioning the head model in order for its at least one second eye position to be consistent with the at least one first eye position given by the PCCR based eye tracker.

A method for conducting a three dimensional (3D) video conference between multiple participants, the method may include determining, for each participant and multiple times during the 3D video conference, updated 3D participant representation information within the virtual 3D video conference environment; and generating, for at least one participant and multiple times during the 3D video conference, an updated representation of a virtual 3D video conference environment, the updated representation of virtual 3D video conference environment represents the updated 3D participant representation information for at least some of the multiple participants; and wherein the 3D participant representation information comprises a 3D model and one or more texture maps.