Video Tracking with Perceptual Hashing for Game Event Tagging
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Solution Overview
Problem
It is difficult for users to simultaneously play video games and selectively record exciting or surprising content while also summarizing game progress, as identifying key events within hours of user-directed gameplay can be challenging.
Innovation Solution
An apparatus and method for video tracking that uses perceptual hashing to detect scene cuts and identify notable events in video game footage, utilizing a database of hashes to efficiently recognize and tag significant in-game moments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually review hours of game footage to identify key events, then event identification accuracy is improved, but time consumption and user effort increase significantly
Solution Approach 1:
The system performs self-service by automatically analyzing game footage to identify key events without requiring manual user review. The automated event detection system processes gameplay videos, detects notable moments, and generates summaries independently, eliminating the need for users to manually scan through hours of content while maintaining accurate event identification.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. Instead of users manually watching and identifying events, the system uses image processing, scene cut detection, and automated recognition algorithms to identify key events, substituting human mechanical review with automated digital processing that is both accurate and time-efficient.
2Measurement precision
If users manually select and record exciting moments during gameplay, then recording precision is improved, but ease of operation deteriorates due to the complexity of simultaneous gameplay and recording
Solution Approach 1:
The system performs self-service by automatically detecting and selecting exciting moments during gameplay without requiring user intervention. The automated system monitors gameplay, identifies notable events, and prepares recordings independently, making the process as easy as simply playing the game while the system handles the complex tasks of moment detection and recording selection.
Solution Approach 2:
The system performs preliminary action by pre-processing and analyzing gameplay footage in advance to identify potential exciting moments before the user needs to review or share them. The system continuously monitors gameplay, pre-identifies notable events, and prepares recordings so that when users want to share or review, the content is already organized and ready, eliminating the need for manual selection during or after gameplay.
3Reliability
If the system processes every frame of game footage to ensure complete event capture, then event detection completeness is improved, but processing speed and productivity deteriorate
Solution Approach 1:
The system applies segmentation by dividing the continuous game footage into discrete temporal segments or clips based on detected scene cuts and notable events. Instead of processing every frame uniformly, the system identifies key transition points and segments the video into meaningful chunks, processing only the relevant segments in detail while maintaining complete event capture. This segmentation approach ensures reliability without the computational burden of frame-by-frame analysis of entire lengthy gameplay sessions.
Data Source
AI summary
A method of identifying text that appears within a sequence of images comprises the steps of defining one or more regions of respective images in which text will appear, detecting when text has appeared in one or more of the defined regions, identifying text within a defined region when text has been detected there, and selecting for output identified text that has appeared, or a respective image of the sequence of images that comprise such identified text.