Sports Broadcast Highlight Detection With Real-Time Metadata Sync
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Solution Overview
Problem
Existing television systems lack the capability to automatically generate and synchronize video highlights with real-time metadata for sports events, limiting interactive and enhanced programming experiences.
Innovation Solution
A system and method that utilizes computer vision techniques to detect replay sequences, network logos, faces, and repetitive text in live sports broadcasts, generating video highlights with associated metadata in real-time, and delivering them to various devices for synchronized viewing and interactive applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated video analysis is implemented to generate highlights and metadata, then productivity and user experience are improved, but device complexity and computational requirements increase
Solution Approach 1:
The video processing system is divided into multiple independent modules: video frame analysis module, logo detection module, face recognition module, text extraction module, highlight generation module, and metadata association module. Each module performs a specific function and can be processed independently, enabling parallel computation and improving overall productivity while managing system complexity through modular design.
Solution Approach 2:
The system performs preliminary analysis of video frames by pre-processing incoming video data to extract key features (logos, faces, text) before full highlight generation. This preliminary action prepares the data in advance, allowing faster highlight creation and metadata association when needed, thereby improving productivity without proportionally increasing complexity.
2Measurement precision
If real-time video frame analysis is performed to detect logos, faces, and text, then measurement precision and highlight accuracy are improved, but processing time and computational load increase
Solution Approach 1:
The system performs video frame analysis at periodic intervals rather than continuously processing every frame. Key frames are selected based on detected changes or at regular time intervals, allowing the system to maintain high detection accuracy for logos, faces, and text while significantly reducing processing time and computational load compared to continuous full-frame analysis.
Solution Approach 2:
The system applies partial analysis to video frames by focusing computational resources on detecting specific elements (logos, faces, text) rather than analyzing all visual content. This selective approach maintains measurement precision for critical event detection while reducing overall processing time by ignoring less important frame details.
3Loss of information
If comprehensive metadata is associated with video highlights, then information completeness and user interaction are improved, but data processing complexity and storage requirements increase
Solution Approach 1:
The system uses a universal metadata structure that can accommodate multiple types of information (event type, timestamp, participant identities, network information, textual content) in a standardized format. This universal approach allows comprehensive information to be associated with highlights without proportionally increasing processing complexity, as the same metadata framework handles all information types consistently.
Solution Approach 2:
The system creates simplified copies or references to the original video content and metadata rather than storing complete high-definition versions of all data. Metadata contains essential information in condensed form, and video highlights are stored at optimized resolutions, reducing storage requirements and data processing complexity while maintaining information completeness for user interaction.
Data Source
AI summary
One or more highlights of a video stream may be identified. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. According to one method, at least a portion of the video stream may be stored. The portion of the video stream may be compared with templates of a template database to identify the one or more highlights. Each highlight may be a subset of the video stream that is deemed likely to match the one or more templates. The highlights, an identifier that identifies each of the highlights within the video stream, and/or metadata pertaining particularly to the one or more highlights may be stored to facilitate playback of the highlights for the users.