Deep Learning Sports Event Analysis System
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Solution Overview
Problem
Current methods for analyzing sporting events require human observers, which are costly and inefficient, and automated audiovisual production lacks advanced analysis capabilities.
Innovation Solution
A deep learning-based system that automatically determines game events by processing audiovisual streams using modular deep learning modules for video and audio data, enabling precise event detection and camera guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human observers are used to judge game events, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical system of human observers with an automated computer vision system using deep learning modules. The system processes audiovisual streams through multiple specialized deep learning modules that detect occurrences, determine game events, and generate camera guidance without human intervention, thereby eliminating the need for expensive human observers while maintaining detection accuracy.
Solution Approach 2:
The system enables self-service by allowing the computer system to automatically perform game event detection and camera guidance generation without requiring human operators. The deep learning modules autonomously process the audiovisual data, identify game events, and generate camera guidance instructions, making the system self-sufficient and eliminating dependency on human observers.
2Device complexity
If automated audiovisual production is implemented, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent divides the automated audiovisual processing system into multiple specialized deep learning modules, each responsible for specific tasks such as detecting particular occurrences, determining specific game events, or generating camera guidance. This segmentation allows the system to achieve high measurement precision through specialized processing while keeping overall device complexity manageable through modular architecture.
Solution Approach 2:
The patent combines multiple deep learning modules into an integrated system that processes audiovisual streams comprehensively. By merging the capabilities of occurrence-specific modules, game event-specific modules, and camera guidance modules, the system achieves superior measurement precision that exceeds what any single module could accomplish alone, while maintaining automated operation.
3Measurement precision
If multiple specialized deep learning modules are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the game event analysis into multiple specialized deep learning modules, each focused on detecting specific occurrences or determining particular game events. This segmentation improves measurement precision by dedicating specialized processing to each task while managing complexity through modular design, where each module can be independently trained and optimized.
Solution Approach 2:
The deep learning modules are designed with universal capabilities to process various types of audiovisual data and detect different kinds of occurrences and game events. This multi-functionality allows the same modular architecture to handle diverse sports events and game types, improving measurement precision across multiple applications without proportionally increasing device complexity.
Data Source
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AI summary
A method and system for the automatic analysis of game events using artificial intelligence is described.