Sportsmedia Technology Corporation

Ryan A Zander

James G Painter

Ted C Chen

Robert C Amyx

Josh D Spivak

Graham W Goldbeck

Mark Perry

Graham Wei-Feng Goldbeck

Joshua Spivak

Methods and systems for use in automating or assisting umpiring of a baseball or softball game are described herein. A location of a strike zone is determined based on video images of a batter standing next to home plate captured by a camera. Locations of a ball traveling towards the batter, and locations of the bat being held by the batter, are autonomously tracked using computer vision based on video images captured by at least two cameras having different positions. Additionally, there are autonomous determinations of whether a location of the ball intersects with the strike zone, and whether the batter made a genuine attempt to swing the bat at the ball, and based one at least one of these determinations, there is an autonomous determination of whether a 'strike' or a “ball” occurred. Additionally, an indication of whether a “strike” or a “ball” occurred is autonomously output.

Automated or assisted umpiring of baseball game using computer vision

A plurality of tracking cameras is pointed towards a routine hovering area of an in-the-field sports participant who routinely hovers about that area. Spots within the hovering area are registered relative to a predetermined multi-dimensional coordinates reference frame (e.g., Xw, Yw, Zw, Tw) such that two-dimensional coordinates of 2D images captured by the tracking cameras can be converted to multi-dimensional coordinates of the reference frame. A body part recognizing unit recognizes 2D locations of a specific body part in the 2D captured images and a mapping unit maps them into the multi-dimensional coordinates of the reference frame. A multi-dimensional curve generator then generates a multi-dimensional motion curve describing motion of the body part based on the mapped coordinates (e.g., Xw, Yw, Zw, Tw). The generated multi-dimensional motion curve is used to discover cross correlations between play action motions of the in-the-field sports participant and real-world sports results.

Determining X,Y,Z,T biomechanics of moving actor with multiple cameras

A plurality of high speed tracking cameras is pointed towards a routine hovering area of an in-the-field sports participant who routinely hovers about that area. Spots within the hovering area are registered relative to a predetermined multi-dimensional coordinates reference frame (e.g., Xw, Yw, Zw, Tw) such that two-dimensional coordinates of 2D images captured by the high speed tracking cameras can be converted to multi-dimensional coordinates of the reference frame. A body part recognizing unit recognizes 2D locations of a specific body part in the 2D captured images and a mapping unit maps them into the multi-dimensional coordinates of the reference frame. A multi-dimensional curve generator then generates a multi-dimensional motion curve describing motion of the body part based on the mapped coordinates (e.g., Xw, Yw, Zw, Tw). The generated multi-dimensional motion curve is used to discover cross correlations between play action motions of the in-the-field sports participant and real-world sports results.

Methods and systems for use in automating or assisting umpiring of a baseball or softball game are described herein. A location of a strike zone is determined based on video images of a batter standing next to home plate captured by a camera. Locations of a ball traveling towards the batter, and locations of the bat being held by the batter, are autonomously tracked using computer vision based on video images captured by at least two cameras having different positions. Additionally, there are autonomous determinations of whether a location of the ball intersects with the strike zone, and whether the batter made a genuine attempt to swing the bat at the ball, and based one at least one of these determinations, there is an autonomous determination of whether a 'strike' or a 'ball' occurred. Additionally, an indication of whether a &#x201c;strike&#x201d; or a &#x201c;ball&#x201d; occurred is autonomously output.

A plurality of high speed tracking cameras are pointed towards a routine hovering area of an in-the-field sports participant who routinely hovers about that area. Spots within the hovering area are registered relative to a predetermined multi-dimensional coordinates reference frame (e.g., Xw, Yw, Zw, Tw) such that two-dimensional coordinates of 2D images captured by the high speed tracking cameras can be converted to multi-dimensional coordinates of the reference frame. A body part recognizing unit recognizes 2D locations of a specific body part in the 2D captured images and a mapping unit maps them into the multi-dimensional coordinates of the reference frame. A multi-dimensional curve generator then generates a multi-dimensional motion curve describing motion of the body part based on the mapped coordinates (e.g., Xw, Yw, Zw, Tw). The generated multi-dimensional motion curve is used to discover cross correlations between play action motions of the in-the-field sports participant and real-world sports results.

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Burlingame, CA, United States of America

Ryan A Zander