Intel Corporation

Xiaofeng Tong

Glen J Anderson

Wenlong Li

Yangzhou Du

Qiang Li

Yimin Zhang

Yikai Fang

Qiang Eric Li

Wei Hu

Chen Ling

Thomas Sachson

Yumeng Wang

Tae-Hoon Kim

Minje Park

Ming Lu

Lidan Zhang

Xiaolu Shen

Jianguo Li

Myung-Ho Ju

Jihyeon Kate Yi

Dayong Ding

JianSheng Chen

Steven Thomas Holmes

Shaohui Jiao

Haihua Lin

Jose E Lorenzo

Mary Smiley

Alok Mishra

Peng Wang

Changcheng Li

Yunzhen Wang

Johnny C Yip

Fucen Zeng

Liwei Liao

Tao Wang

Anbang Yao

Lin Xu

Yurong Chen

Aamer Jaleel

Doron T Houminer

Yuanzhang Chang

Jianbo Ye

Hongzhi Tao

Caleb J Ozer

Jose Elmer S Lorenzo

Yiwei He

Amir Atzmoni

Ofer Shkedi

Margaret E Morris

A system () includes multiple cameras () disposed about an area (), a processor (), and a memory () communicatively coupled to the processor. The memory stores instructions that cause the processor to receive a set of video data () associated with the cameras. In an embodiment, the set of video data includes a set of image frames associated with a set of ball tracking data (). In an embodiment, the operations include selecting a first image frame () associated with a first change in acceleration and a second image frame () associated with a second change in acceleration. In an embodiment, the operations include generating a set of virtual camera actions () based on the first image frame and the second image frame.

Systems and methods for virtual camera highlight creation

Methods, systems and apparatuses may provide for technology that detects an individual in a real-time multi-camera video feed and generates three-dimensional (3D) skeletal data based on the real-time multi-camera video feed. The technology may also automatically identify a frontal body orientation of an individual based on the 3D skeletal data and one or more anthropometric constraints.

Technology to automatically identify the frontal body orientation of individuals in real-time multi-camera video feeds

Embodiments are generally directed to methods and apparatuses for determining a frontal body orientation. An embodiment of a method for determining a three-dimensional (3D) orientation of frontal body of a player comprises: detecting each of a plurality of players in each of a plurality of frames captured by a plurality of cameras; for each of the plurality of cameras, tracking each of the plurality of players between continuous frames captured by the camera; and associating the plurality of frames captured by the plurality of cameras to generate the 3D orientation of each of the plurality of players.

Method and apparatus for determining a frontal body orientation

Techniques related to validating an image based 3D model of a scene are discussed. Such techniques include detecting an object within a captured image used to generate the scene, projecting the 3D model to a view corresponding to the captured image to generate a reconstructed image, and comparing image regions of the captured and reconstructed images corresponding to the object to validate the 3D model.

Automatic point cloud validation for immersive media

Methods, systems and apparatuses may provide for technology that selects a player from a plurality of players based on an automated analysis of two-dimensional (2D) video data associated with a plurality of cameras, wherein the selected player is nearest to a projectile depicted in the 2D video data. The technology may also track a location of the selected player over a subsequent plurality of frames in the 2D video data and estimate a location of the projectile based on the location of the selected player over the subsequent plurality of frames.

Technology to automatically locate ball-holding players in multi-camera video feeds

Example gesture matching mechanisms are disclosed herein. An example machine readable storage device or disc includes instructions that, when executed, cause programmable circuitry to at least: prompt a user to perform gestures to register the user, randomly select at least one of the gestures for authentication of the user, prompt the user to perform the at least one selected gesture, translate the gesture into an animated avatar for display at a display device, the animated avatar including a face, analyze performance of the gesture by the user, and authenticate the user based on the performance of the gesture.

Gesture matching mechanism

A mechanism is described for facilitating real-time multi-view detection of objects in multi-camera environments, according to one embodiment. A method of embodiments, as described herein, includes mapping first lines associated with objects to a ground plane; and forming clusters of second lines corresponding to the first lines such that an intersection point in a cluster represents a position of an object on the ground plane.

Real-time multi-view detection of objects in multi-camera environments

Embodiments are generally directed to methods and apparatuses for determining a game status. An embodiment of a method for determining a game status comprises: detecting players and a ball in a plurality of continuous frames captured by a plurality of cameras; obtaining tracking data of the players and the ball of the plurality of continuous frames; and determining a game status for each of the plurality of continuous frames based on the tracking data of the players and the ball of the plurality of continuous frames.

Method and apparatus for determining a game status

A multi-camera architecture for detecting and tracking a ball in real-time. The multi-camera architecture includes network interface circuitry to receive a plurality of real-time videos taken from a plurality of high-resolution cameras. Each of the high-resolution cameras simultaneously captures a sports event, wherein each of the plurality of high-resolution cameras includes a viewpoint that covers an entire playing field where the sports event is played. The multi-camera architecture further includes one or more processors coupled to the network interface circuitry and one or more memory devices coupled to the one or more processors. The one or more memory devices includes instructions to determine the location of the ball for each frame of the plurality of real-time videos, which when executed by the one or more processors, cause the multi-camera architecture to simultaneously perform one of a detection scheme or a tracking scheme on a frame from each of the plurality of real-time videos to detect the ball used in the sports event and perform a multi-camera build to determine a location of the ball in 3D for the frame from each of the plurality of real-time videos using one of detection or tracking results for each of the cameras.

Multi-cam ball location method and apparatus

Method, systems and apparatuses may provide for technology that extracts one or more motion features from filtered position data associated with a projectile in a game and identifies a turning point in a trajectory of the projectile based on the one or more motion features. The technology may also automatically designate the turning point as a highlight moment if one or more of the turning point or the trajectory satisfies a proximity condition with respect to a target area in the game.

Highlight moment identification technology in volumetric content creation systems

Generally this disclosure describes a video communication system that replaces actual live images of the participating users with animated avatars. A method may include selecting an avatar; initiating communication; detecting a user input; identifying the user input; identifying an animation command based on the user input; generating avatar parameters; and transmitting at least one of the animation command and the avatar parameters.

Communication using interactive avatars

Examples of systems and methods for augmented facial animation are generally described herein. A method for mapping facial expressions to an alternative avatar expression may include capturing a series of images of a face, and detecting a sequence of facial expressions of the face from the series of images. The method may include determining an alternative avatar expression mapped to the sequence of facial expressions, and animating an avatar using the alternative avatar expression.

Augmented facial animation

A video capture and processing system includes a memory configured to store a pose database. The pose database includes poses that indicate a start or stoppage in an event. The system also includes a processor operatively coupled to the memory. The processor is configured to generate a pose of an individual in a video frame of captured video of the event. The pose can be three-dimensional pose or a two-dimensional pose. The processor is also configured to determine, based on the pose database, whether the pose of the individual indicates a start or a stoppage in the event. The processor is further configured to control an upload of video of the event based on the determination of whether the pose indicates the start or the stoppage in the event.

Method and apparatus of game status determination

An example apparatus is disclosed herein that includes a memory and at least one processor. The at least one processor is to execute instructions to: select a gesture from a database, the gesture including a sequence of poses; translate the selected gesture into an animated avatar performing the selected gesture for display at a display device; display a prompt for the user to perform the selected gesture performed by the animated avatar; capture an image of the user performing the selected gesture; and perform a comparison between a gesture performed by the user in the captured image and the selected gesture to determine whether there is a match between the gesture performed by the user and the selected gesture.

System and techniques for positional analysis using computer vision sensor synchronization are described herein. A set of sensor data may be obtained for a participant of an activity. A video stream may be captured in response to detection of a start of the activity in the set of sensor data. The video stream may include images of the participant engaging in the activity. A key stage of the activity may be identified by evaluation of the sensor data. A key frame may be selected from the video stream using a timestamp of the sensor data used to identify the key stage of the activity. A skeletal map may be generated for the participant in the key frame using key points of the participant extracted from the key frame. Instructional data may be selected using the skeletal map. The instructional data may be displayed on a display device.

Positional analysis using computer vision sensor synchronization

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Beijing, China

Xiaofeng Tong