Intel Corporation

Nokia Technologies Oy

Nokia Corporation

Yikai Fang

Wenlong Li

Xiaofeng Tong

Qiang Li

Qiang Eric Li

Chen Ling

Tae-Hoon Kim

Kongqiao Wang

Yangzhou Du

Minje Park

Yumeng Wang

Myung-Ho Ju

Jihyeon Kate Yi

Lei Xu

Ming Lu

Jiangwei Li

Lidan Zhang

Xiaolu Shen

Shaohui Jiao

Hongzhi Tao

Fucen Zeng

Hang Zheng

Hao Wang

Roope Takala

Kong Qiao Wang

Xiujuan Chai

Wang Kongqiao

Haihua Lin

Hanqing Lu

Qingshan Liu

Ofer Shkedi

Chenning Liu

Longwei Fang

Techniques related to performing object or person association or correspondence in multi-view video are discussed. Such techniques include determining correspondences at a particular time instance based on separately optimizing correspondence sub-matrices for distance sub-matrices based on two-way minimum distance pairs between frame pairs, generating and fusing tracklets across time instances, and adjusting correspondence, after such tracklet processing, via elimination of outlier object positions and rearrangement of object correspondence.

Multi-camera person association via pair-wise matching in continuous frames for immersive video

A method for trajectory generation based on player tracking is described herein. The method includes determining a temporal association for a first player in a captured field of view and determining a spatial association for the first player. The method also includes deriving a global player identification based on the temporal association and the spatial association and generating a trajectory based on the global player identification.

Player trajectory generation via multiple camera player tracking

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

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

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

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

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

Methods and apparatus to detect collision of a virtual camera with objects in a three-dimensional volumetric model are disclosed herein. An example virtual camera system disclosed herein includes cameras to obtain images of a scene in an environment. The example virtual camera system also includes a virtual camera generator to create a 3D volumetric model of the scene based on the images, identify a 3D location of a virtual camera to be disposed in the 3D volumetric model, and detect whether a collision occurs between the virtual camera and one or more objects in the 3D volumetric model.

Methods and apparatus to detect collision of a virtual camera with objects in three-dimensional volumetric model

It is inter alia disclosed to classify samples of a set of samples of an image regarding whether or not a respective sample comprises optical information on a specific object type based on classification information. Therein, the classification information comprises a plurality of classifiers, each classifier being associated exclusively with one color channel of a plurality of color channels.

Classifying image samples

A method and system for invoking an operation of a communication terminal in response to registering and interpreting a predetermined motion or pattern of an object. An input is received, the image data of the object is captured and the object in the image data is identified.

Command input by hand gestures captured from camera

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Beijing, China

Yikai Fang