Automated Frontal Body Orientation Detection in Multi-Camera Feeds
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Solution Overview
Problem
Existing solutions for presenting immersive sports media experiences from a player's perspective in real-time multi-camera video feeds are costly and time-consuming due to the need for manual labeling of virtual camera positions and orientations.
Innovation Solution
An automated system that uses 3D skeletal data and anthropometric constraints to identify the frontal body orientation of individuals in real-time multi-camera video feeds, eliminating the need for manual analysis and enabling efficient generation of immersive video frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of virtual camera position and orientation is used, then accuracy of player perspective can be achieved, but time consumption and cost increase significantly
Solution Approach 1:
The system enables automatic self-labeling by having the player's own body orientation and position data directly determine the virtual camera parameters. The player's skeletal tracking data automatically provides the camera position and orientation without requiring external manual annotation, thus achieving both high accuracy and efficiency
Solution Approach 2:
The patent creates a virtual copy of the player's body orientation and position to define the virtual camera parameters. By copying the player's anatomical data (shoulder position, head orientation, torso angle) and mapping it to camera coordinates, the system achieves accurate player perspective automatically without manual labeling
2Measurement precision
If manual labeling of virtual camera position and orientation is used, then accuracy of player perspective can be achieved, but cost increases significantly
Solution Approach 1:
The system eliminates the need for expensive manual annotation services by enabling automatic self-labeling. The player's own tracking data serves as the source for camera parameters, removing the requirement for human annotators and significantly reducing production costs while maintaining accuracy
Solution Approach 2:
The patent replaces the manual mechanical process of labeling with an automated computational system. By substituting human annotators with algorithmic processing of skeletal tracking data, the system achieves both cost reduction and maintained precision in player perspective generation
3Productivity
If automated identification of frontal body orientation is implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The system uses a unified skeletal tracking framework that serves multiple functions: it provides both the player's position and orientation data, and simultaneously generates the virtual camera parameters. This multi-functional approach increases productivity while managing complexity by reusing the same data source and processing pipeline for multiple purposes
Data Source
AI summary
Methods, systems and apparatuses may provide for technology that detects an individual in a real-time multi-camera video feed and generates three-dimensional (3D) skeletal data based on the real-time multi-camera video feed. The technology may also automatically identify a frontal body orientation of an individual based on the 3D skeletal data and one or more anthropometric constraints.