Progressivistic Metadata Collection via Automated Video Analysis
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Solution Overview
Problem
Current sports analytics systems lack comprehensive and efficient methods for collecting and processing progressivistic metadata, which is essential for training, practicing, and analyzing human activities such as sports, e-sports, and other performance-based events, as they often rely on manual logging and lack real-time, automated data analysis.
Innovation Solution
A system and method for collecting and processing progressivistic metadata that includes a timecode database, a video files database, a server for generating metadata files, and automated modules for encoding and quality assurance, utilizing machine learning for real-time or near real-time data analysis and logging, enabling the synchronization of events with timecodes and performance data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual logging methods are used for collecting sports metadata, then the system complexity is reduced, but the productivity and measurement precision of data collection deteriorate
Solution Approach 1:
The patent replaces manual logging (mechanical human operation) with automated computer vision systems and machine learning algorithms. The system uses automated detection of sports events, player tracking, and metadata generation through AI models, eliminating the need for manual data entry while significantly improving productivity and measurement precision.
Solution Approach 2:
The system enables self-service data collection by automatically processing video feeds, detecting events, and generating metadata without human intervention. The automated pipelines include event detection, player identification, and statistical calculation that all occur autonomously, allowing the system to serve itself in data collection tasks.
2Measurement precision
If real-time automated data analysis is implemented, then the productivity and measurement precision improve, but the device complexity and computational resources required worsen
Solution Approach 1:
The patent divides the complex data analysis system into modular components: video processing modules, event detection modules, player tracking modules, and metadata generation modules. Each module handles specific tasks independently, reducing overall system complexity while maintaining high measurement precision through specialized processing in each segment.
Solution Approach 2:
The system introduces intermediate data structures and processing layers between raw video input and final metadata output. These intermediaries include detected objects, tracked entities, and event candidates that facilitate gradual transformation of data, making the complex processing pipeline more manageable and maintainable while improving accuracy.
3Loss of information
If comprehensive metadata collection is performed, then the quantity and quality of information improve, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary processing of video data during the event itself, extracting and storing key metadata as it occurs. Player positions, event detections, and basic statistics are captured in real-time or near real-time, allowing comprehensive data collection without significant time loss during post-processing.
Solution Approach 2:
The patent implements continuous data collection and processing pipelines that operate throughout the entire sports event. Rather than batch processing after the event, the system continuously analyzes video feeds, detects events, and generates metadata without interruption, eliminating time losses associated with post-event processing.
Data Source
AI summary
Systems, methods and computer readable storage media for collecting and processing progressivistic metadata are described; a structure of files comprising a progressivistic metadata and implementational aspects of various uses thereof are further described.