A System for Tracking, Locating and Calculating the Position of a First Moving Object in Relation to a Second Object

1 . A computerized system for automatic calculation of the relative location of a first object relative to a target object, wherein at least the first object is in motion,

the system comprising a first camera and a second camera, positioned in relation to the target object such that the location of the target object defines the target vertex of a triangle and the first and second cameras define the other two vertices of the triangle, such that an internal angle at the target vertex of the triangle is between 45 degrees and 135 degrees;

wherein the first camera is positioned within view of the target object, and a side camera is positioned to the left or right of the target object,

wherein each camera captures a continuous video image; wherein the field of view of the first camera is at least 120 degrees and includes the target object and the first object once it enters the field of view,

and wherein the field of view of the side camera is at least 120 degrees, and also includes the target object and the first object once it enters the field of view;

wherein both cameras are in functional communication with, and transmit video data to a computer programmed with one of more mapping and tracking backbone programs and an AI algorithm, receiving data from the backbone program, which is trained to classify the first object using the data provided by the one or more backbone programs,

wherein one or more mapping backbone programs defines and maps 3-dimensional space in real-time, centered on the target object, and performs object detection, monitors and analyses the video images, and post-processes the video data from the cameras, and feeds it into the AI algorithm,

and wherein the one or more tracking backbone programs detects the first object at a time T=1 and calculates the speed and direction of the first object, and predicts the path of travel of the first object relative to the target object, and thereby predicts a position of the first object relative to the target object at a time T=2, and produces data containing such information, and feeds said data to the AI algorithm which is trained to classify the first object into various categories,

and wherein the classification category data is transmitted from the computer to a screen viewable by a viewer or to a computer programmed to select a response based on the classification of the first object.

2 . The computerized system of claim 1 wherein the one or more mapping backbone programs employs a bounding box and a confidence prediction class probability map.

3 . The computerized system of claim 1 , wherein the one or more tracking backbone programs is in functional communication with a Doppler radar system poisoned at the location of the first camera, wherein said Doppler radar system provides real-time velocity information to said one or more tracking backbone programs.

4 . The computerized system of claim 3 wherein said computerized system is alerted to the presence of the first object by an alert from the Doppler radar system which sends an alert when it detects an object traveling at a velocity above a set threshold velocity.

5 . The computerized system of claim 1 wherein the classification category data is transmitted from the computer to a screen viewable by a viewer and wherein classification category data is ranked by threat level and is presented visually.

6 . The computerized system of claim 5 wherein the screen viewable by a viewer further displays information including a probability of impact and a time to impact of the first object relative to the target object.

7 . The system of claim 1 further comprising real-time input data from a satellite, drone or aircraft in functional communication with, and transmitting data to a computer programmed with one of more backbone programs and an AI algorithm, receiving data from the backbone programs.

8 . The system of claim 1 adapted to a sports game involving a ball in motion, wherein the first object is a ball in motion.

9 . The system of claim 8 wherein the game is baseball or softball or rounders or cricket, and the target object is a player.

10 . The system of claim 9 wherein the game is baseball on a baseball field having a home plate, a pitcher's mound, a first base, a second base, and a third base,

wherein one or more backbone programs define and map a 3-dimensional space, wherein a home plate space is a 3-dimensional shape extrapolated from an N×N 2-dimensional grid centered on the home plate, and performs object detection, monitors and analyses the video images, and post-processes the video data from the cameras, and feeds it into the AI algorithm, and

wherein the one or more backbone programs detects and maps the ball in real-time, and calculates the speed of the ball and calculates and/or predicts the path of travel of the ball relative to the home plate space and predicting the entry and exit points of the ball relative to the home plate space, and produces data containing such information, and feeds said data to the AI algorithm which is trained to classify a ball into various pitch types using the data provided by the one or more backbone programs, and

wherein the relative location of the ball in motion relative to the player is transmitted from the computer to a screen viewable by a viewer.

11 . The system of claim 9 wherein the AI algorithm is trained using information related to the motion of a player.

12 . The system of claim 11 wherein the player is a pitcher.

13 . The system of claim 12 wherein the transmission of the video data from the one or more cameras to the computer programmed with one of more mapping and tracking backbone programs and an AI algorithm, is automatically activated upon a defined motion of the pitcher.

14 . The system of claim 13 wherein the defined motion of the pitcher is a wind-up prior to a pitch.

15 . The system of claim 12 wherein a background behind the player is blocked or masked.