Data processing method, system and computer program product in video production of a live event

A data processing system in video production of a live event involving a plurality of persons acting in a real-world target field, the data processing system comprising:

• a data receiving unit configured to receive wireless communication signals that contain respective sensor data obtained for the plurality of persons, the sensor data including motion-triggered data for each person;
• a sensor data processing unit configured to process the motion-triggered data in the received wireless communication signals to determine respective first movement patterns for at least some of the plurality of persons as they act in the real-world target field;
• a video obtaining unit configured to obtain a video stream of the real-world target field;
• a video processing unit configured to process the video stream to determine respective second movement patterns for at least some of the plurality of persons as they act in the real-world target field;
• a movement pattern analysis unit configured to analyze the first movement patterns and the second movement patterns for matches between them;
• and
• a pattern identification unit configured to, for each particular second movement pattern that matches a particular first movement pattern, identify a person having the particular second movement pattern as being associated with the sensor data that comprises the motion-triggered data from which the particular first movement pattern was determined;
• wherein the movement pattern analysis unit is configured such that analyzing of the first movement patterns and the second movement patterns for matches between them comprises:
• generating a machine learning based correlation factor, the machine learning based correlation factor being trained on said motion-triggered data and said video stream, the machine learning based correlation factor recognizing one or more correlations between the first and second movement patterns;
• and
• associating the one or more correlations with a relevance level including normalized values of different actions associated with the plurality of persons acting in the real-world target field, wherein said one or more correlations are sorted based on respective relevance levels;
• wherein the motion-triggered data in the sensor data as well as video frames of the obtained video stream comprise time stamps, and wherein the analyzing of the first movement patterns and the second movement patterns for matches between them is temporally confined by use of the time stamps, and
• wherein based on the machine learning based correlation factor, the movement pattern analysis unit is further configured to:
• define a pattern matching threshold as a predetermined value being indicative of a minimum performance requirement of the analyzing step;
• and
• adjust the time stamps of the motion-triggered data and/or the video frames upon the machine learning based correlation factor being below the pattern matching threshold.

A non-volatile computer program product stored on a tangible computer readable medium and comprising computer code for performing a data processing method when the computer program code is executed by a processing device, the data processing method being in video production of a live event involving a plurality of persons acting in a real-world target field, the data processing method comprising:

• receiving wireless communication signals that contain respective sensor data obtained for the plurality of persons, the sensor data including motion-triggered data for each person;
• processing the motion-triggered data in the received wireless communication signals to determine respective first movement patterns for at least some of the plurality of persons as they act in the real-world target field;
• obtaining a video stream of the real-world target field;
• processing the video stream to determine respective second movement patterns for at least some of the plurality of persons as they act in the real-world target field;
• analyzing the first movement patterns and the second movement patterns for matches between them;
• and
• for each particular second movement pattern that matches a particular first movement pattern, identifying a person having the particular second movement pattern as being associated with the sensor data that comprises the motion-triggered data from which the particular first movement pattern was determined;
• wherein the analyzing of the first movement patterns and the second movement patterns for matches between them comprises:
• generating a machine learning based correlation factor, the machine learning based correlation factor being trained on said motion-triggered data and said video stream, the machine learning based correlation factor recognizing one or more correlations between the first and second movement patterns;
• and
• associating the one or more correlations with a relevance level including normalized values of different actions associated with the plurality of persons acting in the real-world target field, wherein said one or more correlations are sorted based on respective relevance levels;
• wherein the motion-triggered data in the sensor data as well as video frames of the obtained video stream comprise time stamps, and wherein the analyzing of the first movement patterns and the second movement patterns for matches between them is temporally confined by use of the time stamps, and
• wherein based on the machine learning based correlation factor, the method further comprises:
• defining a pattern matching threshold as a predetermined value being indicative of a minimum performance requirement of the analyzing step;
• and
• adjusting the time stamps of the motion-triggered data and/or the video frames upon the machine learning based correlation factor being below the pattern matching threshold.