Player Identification System Video Content Analysis
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Solution Overview
Problem
The increasing volume of video content related to gaming online, particularly from live streaming and user-uploaded content, makes it difficult to manage and identify relevant information such as the game, characters, and players, hindering user experience and content discovery, as well as player exposure and public image management.
Innovation Solution
A player identification system and method that utilizes image and audio analysis, including pre-processing, character recognition, and metadata analysis to identify player names in video content, employing techniques like optical character recognition, machine learning, and contextual indicators to generate data associating video content with player profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large quantities of video content are transmitted and shared between users, then the ability to share gaming experiences is improved, but content management and identification become harder
Solution Approach 1:
The system performs preliminary analysis of video content during upload by extracting frames, performing optical character recognition on UI elements, and identifying player names before the content is fully processed. This preliminary action creates structured data associations early in the workflow, making subsequent content management and retrieval operations simpler and more efficient.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between raw video content and the content management system. This intermediary system performs automated analysis, extracts player identifiers, and creates structured metadata that bridges the gap between unstructured video data and the organized content database, enabling efficient management of large content volumes.
2Ease of operation
If video content is uploaded without associated identification information, then upload simplicity is improved, but content discovery and location become difficult
Solution Approach 1:
The system implements self-service functionality where the video content itself provides the identification information needed. By performing optical character recognition on UI elements within the video frames and automatically extracting player names and game information, the content uploads for itself the metadata it needs, eliminating the requirement for manual information entry while preserving complete identification data.
Solution Approach 2:
The system performs preliminary extraction and association of identification information during the upload process itself. By analyzing frames, recognizing text, and identifying player names before the content is fully indexed, the system ensures that identification information is captured automatically, maintaining upload simplicity while preventing loss of critical content metadata.
3Productivity
If automated player identification is implemented, then content organization is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively analyzing only specific frames from the video content rather than processing every frame. It targets keyframes or frames containing UI elements where player names are likely to appear, performing optical character recognition only on relevant portions of the video. This approach maintains effective content organization while significantly reducing the overall processing time and computational resources required.
Data Source
AI summary
A player identification system for identifying players from recorded video content, the system including a video content analysis unit operable to analyse one or more frames of video content, a player name identification unit operable to identify player names in one or more analysed frames of the video content, and a data generation unit operable to generate data relating to the video in dependence upon one or more identified player names.