Media Stream Annotator for Sports Event Timing
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Solution Overview
Problem
The existing systems for annotating media streams, such as live sports events, are inefficient and error-prone, requiring significant human effort and time, with Pattern Recognition Systems (PRS) operating with less than absolute accuracy due to noise and requiring Ground Truth Metadata (GTM) validation by human annotators.
Innovation Solution
A Media Stream Annotator (MSA) system with a Human-Computer Interface (HCI) that automatically generates GTM, adjusts PRS input parameters, and optimizes recognition accuracy by using Third Party Metadata and Human Annotations to reduce human effort and improve accuracy, enabling real-time and archived media processing with graphic overlays and dynamic parameter adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotators manually create Ground Truth Metadata, then accuracy and validation are improved, but time consumption and labor effort increase significantly
Solution Approach 1:
The system enables human annotators to work more efficiently by having the system automatically generate proposed annotations that annotators can quickly review and approve with minimal manual input. The Human-Computer Interface allows annotators to validate and correct automated results with simple interactions, making the system serve itself while maintaining human oversight for accuracy.
Solution Approach 2:
The system implements a feedback loop where automated Pattern Recognition Systems generate proposed annotations, human annotators review and correct them, and the corrected annotations are fed back into the system to improve future automated generation. This continuous feedback mechanism reduces the time needed for each annotation while maintaining high accuracy through human validation.
2Productivity
If Pattern Recognition Systems process media streams automatically, then productivity is improved, but recognition accuracy decreases due to noise and errors
Solution Approach 1:
The system introduces human annotators as an intermediary layer between the automated Pattern Recognition System and the final Ground Truth Metadata. Human annotators review and correct the automated annotations, acting as a mediator that filters out errors and noise while maintaining the high productivity benefits of automation. This intermediary validation step ensures accuracy without requiring complete manual annotation.
3Measurement precision
If PRS input parameters are adjusted to optimize recognition accuracy, then measurement precision is improved, but system complexity increases
Solution Approach 1:
The system dynamically adjusts PRS input parameters based on the specific media stream characteristics and annotation requirements. Rather than using fixed complex parameter sets, the system adapts parameters in real-time during the annotation process, simplifying the overall system complexity while maintaining high recognition accuracy through flexible, context-aware parameter adjustment.
Data Source
AI summary
A method to correct for temporal variability in incoming streams of media and data to optimize the performance of a pattern recognition system includes the steps of receiving from one of the incoming streams a point in time when an event is announced, applying probability distribution about the point in time, shifting a point of highest probability of the probability distribution back in time by an amount effective to accommodate for a delay between the event and the announcement, comparing a detected pattern of the event to a stored pattern of similar events and applying a confidence value to the comparison, and confirming to the pattern recognition system that the event occurred at the point of highest probability when the confidence score exceeds a predefined threshold. The method is useful to determine the time at which a particular play occurs during a sporting event, such as the time of a shot-on-goal in a soccer match.