Virtual Camera Configuration via Machine Learning
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Solution Overview
Problem
Manual configuration of virtual cameras in live broadcasting is costly and time-consuming, limiting the ability to provide varied and optimal views of events.
Innovation Solution
A machine learning model is used to automatically determine virtual camera presets based on historical game data, broadcast camera data, and audiovisual information, generating optimal camera positions, orientations, and movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration of virtual cameras is performed, then camera positioning and setup can be done with full control, but the process becomes costly and time-consuming
Solution Approach 1:
The system enables self-service by automatically configuring virtual cameras using machine learning models that analyze event data and generate optimal camera placements and movements without human intervention, eliminating the time-consuming manual setup process while maintaining quality results
Solution Approach 2:
The patent replaces the mechanical manual operation system with an automated machine learning-based system. The ML model processes event data, determines optimal camera configurations, and controls virtual cameras automatically, substituting human operators with an intelligent automated system that reduces setup time while maintaining or improving configuration quality
2Ease of operation
If manual configuration of virtual cameras is performed, then detailed control over each camera is achieved, but operational costs increase
Solution Approach 1:
The system performs self-service by automatically generating and executing camera configurations through machine learning models, eliminating the need for human operators and thereby reducing operational costs while maintaining detailed control over camera behavior through algorithmic decision-making
Solution Approach 2:
The patent substitutes the human-operated mechanical control system with an automated machine learning system that makes configuration decisions. This replacement eliminates labor costs associated with manual operation while maintaining precise control over camera positioning and behavior through intelligent algorithms
3Productivity
If automated machine learning configuration is used, then time and costs are reduced, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between event data and camera control. This intermediary processes complex data analysis and decision-making tasks, enabling rapid automated configuration while managing system complexity by encapsulating the intelligence within a trained model rather than requiring complex real-time control logic
Data Source
AI summary
A virtual camera configuration system includes any number of cameras disposed about an area, such as an event venue. The system also includes at least one processor and at least one non-transitory, computer-readable medium communicatively coupled to the at least one processor. In certain embodiments, the at least one non-transitory, computer-readable medium is configured to store instructions which, when executed, cause the processor to perform operations including receiving a set of game data, receiving a set of audiovisual data, and receiving a set of camera presets. The operations also include generating a set of training data and training a model based on the set of training data. The operations also include generating, using the model on a second set of game data and a second set of audiovisual data, a second set of camera presets associated with the set of virtual cameras.