Machine Learning Participant Tracking for Event Viewing
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Solution Overview
Problem
Viewers, especially family members of participants in events such as sports or artistic performances, often lack understanding of the event, hindering their ability to enjoy and follow the activities, particularly when focusing on a specific participant.
Innovation Solution
A system utilizing machine learning to identify and track a targeted participant within event images or videos, providing relevant information and enhancing the viewing experience by transforming a smartphone or tablet into an informative tool, allowing viewers to access statistics, capture photos, and share event highlights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If viewers rely on traditional viewing methods without additional information, then the viewing experience is simple and straightforward, but viewers lack understanding of the event and cannot effectively follow participant activities
Solution Approach 1:
The system transforms a conventional smartphone or tablet into a multi-functional event viewing device by integrating camera capabilities, machine learning processing, and information display functions. The device serves both as a standard mobile device and as an intelligent event analysis tool, eliminating the need for specialized equipment while providing comprehensive participant tracking and event understanding capabilities.
Solution Approach 2:
The system introduces an intermediary processing layer between the event and the viewer. The machine learning module acts as a mediator that captures event images, identifies participants, retrieves relevant information, and presents it to the viewer in an understandable format, bridging the gap between raw event data and viewer comprehension.
2Loss of information
If the system provides comprehensive information about all participants, then viewers gain complete understanding of the event, but the information becomes overwhelming and difficult to focus on specific participants
Solution Approach 1:
The system applies local quality by allowing viewers to focus on specific participants of interest while maintaining the option to view broader event context. The machine learning module enables selective identification and tracking of individual participants, providing tailored information about chosen subjects without requiring the viewer to process information about all participants simultaneously.
Solution Approach 2:
The system uses visual indicators such as colored overlays or highlights on identified participants within captured images. These visual changes help viewers quickly locate and follow specific participants across the event scene, making participant tracking intuitive and easy to operate.
3Measurement precision
If the system uses traditional image processing methods, then the processing is fast and simple, but the accuracy of participant identification and tracking is insufficient
Solution Approach 1:
The system replaces traditional mechanical or rule-based image processing methods with machine learning-based participant identification. The machine learning module automatically learns to recognize and track participants across images, providing high accuracy without requiring manual configuration or complex processing rules, thereby managing complexity through intelligent automation.
Data Source
AI summary
A system (300) and method (900) for viewing an event (80). The system (300) can enhance the experience of the viewer (120) in a variety of different ways. A machine learning module (534) can be used to train the system (300) to correctly identify the participants (110) of an event (80) from an image (728) or video (729) captured at the event (80).