Sportvision, Inc.

Sportsmedia Technology Corporation

Etak, Inc.

Sportsvision, Inc.

Marvin S White

Richard H Cavallaro

Stanley K Honey

James R Gloudemans

Divya Ramakrishnan

Kenneth Alan Milnes

Walter Hsiao

Rand Pendleton

Terence J O'Brien

Kevin R Meier

Lavanya Sridharan

Louis Gentry

Radford Parker

Roger R Labbe

Jerry Neil Gepner

Vidya Elangovan

William F Squadron

Alan C Phillips

Fred Judson Heinzmann

Alina Alt

Timothy P Heidmann

James Orville McGuffin

Stuart K Neubarth

Matthew T Lazar

Nicholas R Kalayjian

Uri Rodny

Zachary Y Deretsky

John LaCroix

A system for registering one or more cameras and/or creating an accurate three-dimensional (3D) model of a world space environment including back projecting at least one image from at least one of a plurality of camera views to the 3D model based on a set of existing camera parameters. The back projected image is added as a texture for the 3D model. This texture is automatically compared to one or more images from other camera views using a color space comparison of images to determine a set of differences or errors. The camera parameters and the 3D model are automatically adjusted to minimized the differences or errors. Over time, the parameters and the 3D model converge on a state that can be used to track moving objects, insert virtual graphics and/or perform other functions.

Multi view camera registration

A sensor system and a method of operating a sensor system including a plurality of sensors tracking a moving object in an area having known bounding surfaces. The apparatus and method calculate a time-specific position of the object based on data and sensor parameters from at least two of the plurality of sensors and determine errors between the calculated time-specific positions calculated. The method and apparatus calculate a minimum system error attributable to the at least two sensors by constraining at least one dimension in the data of the sensor used in the calculated time-specific position of the object associated with the sensor, the constraining based on an object/surface interaction, the minimum system error calculated by solving for modified sensor parameters for each sensor.

Estimating tracking sensor parametrization using known surface constraints

A system for registering one or more cameras and/or creating an accurate three dimensional (3D) model of a world space environment includes back projecting at least one image from at least one of a plurality of camera views to the 3D model based on a set of existing camera parameters. The back projected image is added as a texture for the 3D model. This texture is automatically compared to one or more images from other camera views using a color space comparison of images to determine a set of differences or errors. The camera parameters and the 3D model are automatically adjusted to minimize the differences or errors. Over time, the parameters and the 3D model converge on a state that can be used to track moving objects, insert virtual graphics and/or perform other functions.

The path and/or position of an object is tracked using two or more cameras which run asynchronously so there is need to provide a common timing signal to each camera. Captured images are analyzed to detect a position of the object in the image. Equations of motion for the object are then solved based on the detected positions and a transformation which relates the detected positions to a desired coordinate system in which the path is to be described. The position of an object can also be determined from a position which meets a distance metric relative to lines of position from three or more images. The images can be enhanced to depict the path and/or position of the object as a graphical element. Further, statistics such as maximum object speed and distance traveled can be obtained. Applications include tracking the position of a game object at a sports event.

Tracking an object with multiple asynchronous cameras

An object is detected in images of a live event by storing and indexing templates based on representations of the object from previous images. For example, the object may be a vehicle which repeatedly traverses a course. A first set of images of the live event is captured when the object is at different locations in the live event. A representation of the object in each image is obtained, such as by image recognition techniques, and a corresponding template is stored. When the object again traverses the course, for each location, the stored template which is indexed to the location can be retrieved for use in detecting the object in a current image. The object's current location may be obtained from GPS data from the object, for instance, or from camera sensor data, e.g., pan, tilt and zoom, which indicates a direction in which the camera is pointed.

Detecting an object in an image using templates indexed to location or camera sensors

A representation of an object in an image of a live event is obtained by determining a color profile of the object. The color profile may be determined from the image in real time and compared to stored color profiles to determine a best match. For example, the color profile of the representation of the object can be obtained by classifying color data of the representation of the object into different bins of a color space, in a histogram of color data. The stored color profiles may be indexed to object identifiers, object viewpoints, or object orientations. Color data which is common to different objects or to a background color may be excluded. Further, a template can be used as an additional aid in identifying the representation of the object. The template can include, e.g., a model of the object or pixel data of the object from a prior image.

Identifying an object in an image using color profiles

A video effect is created that provides an experience to a viewer of freezing time during an event that is the subject of a video presentation, investigating the event during that frozen moment in time, and (optionally) resuming the action of the event. During that frozen moment in time, the video can move around the scene of the event and/or zoom in (or out) to better highlight an aspect of the event. In one embodiment, there will be a transition from video captured by a broadcast camera (or another camera) to a high resolution still image, movement around the high resolution still image, and a transition from the high resolution still image back to video from the broadcast camera (or another camera).

Video effect using movement within an image

A representation of an object in an image of a live event is detected by matching potential representation of the object against multiple types of templates. For example, the templates can include monochrome data, chrominance and/or luminance data, pixel data of the object from an earlier image, e.g., as a video template, an edge and morphology based template, a model of the object, or a predetermined static texture which is based on an appearance of the object. A weighting function may also be used. In one possible approach, a first type of template is used in an initial search area, and a second type of template is used in a smaller region of the initial search area. Based on a position of the optimum representation of the object in the image, a graphic can be provided in the image, or sensor and/or registration data of a camera can be updated.

Detecting an object in an image using multiple templates

Growing community of inventors

San Carlos, CA, United States of America

Marvin S White