Video Processing for Player Activity Beyond Numerical Metrics
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Solution Overview
Problem
Existing automatic digest video generation systems struggle to evaluate the degree of player activity in sports games based on plays that do not appear in numerical values, such as fine plays that prevent decisive scores.
Innovation Solution
A video processing device and method that includes a video acquisition, person identification, importance calculation, and importance integration to evaluate player activity based on sports videos, considering plays that do not appear in numerical values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic digest video generation uses only numerical performance values (scores, hits, home runs, etc.), then the evaluation process is simple and objective, but it fails to capture important plays that do not appear in numerical metrics
Solution Approach 1:
The system segments the video into multiple shots and identifies important persons in each shot. By dividing the evaluation into shot-level importance and person-level importance, the system can comprehensively assess plays that aren't captured by traditional numerical metrics while maintaining a structured, manageable process
Solution Approach 2:
The system introduces an intermediary importance evaluation mechanism that bridges the gap between simple numerical metrics and comprehensive play assessment. The importance calculation based on shot duration, position, and temporal characteristics serves as a mediator to capture fine plays without requiring complex manual evaluation
2Measurement precision
If editors manually evaluate and select important plays, then comprehensive evaluation including fine plays is achieved, but the processing time and labor cost increase significantly
Solution Approach 1:
The system enables automatic self-evaluation of video content by computing importance metrics based on objective criteria such as shot duration, player position, and temporal characteristics. This self-service approach allows the system to identify fine plays and important moments without requiring manual editor intervention, significantly reducing processing time while maintaining evaluation accuracy
Solution Approach 2:
The system changes the evaluation parameters from traditional numerical metrics to a multi-dimensional importance assessment that includes shot duration, player position, and temporal characteristics. This parameter transformation enables automatic identification of fine plays without increasing processing time
3Measurement precision
If the system processes entire game videos to identify important plays, then comprehensive coverage is achieved, but the computational load and processing time increase
Solution Approach 1:
The system extracts only the essential features needed for importance evaluation, such as shot duration, player position, and temporal characteristics, rather than processing the entire video content in detail. This extraction approach reduces computational energy while maintaining detection accuracy for identifying fine plays and important moments
Data Source
AI summary
The video processing device includes a video acquisition means, a person identification means, an importance calculation means, and an importance integration means. The video acquisition means acquires a material video. The person identification means identifies a person from the material video. The importance calculation means calculates an importance of the material video. The importance integration means integrates the importance for each person and outputs a person importance indicating an importance for each person.