Gameplay Video Tracking Using Scene Cuts and Perceptual Hashing
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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 existing systems are time-consuming and inefficient in identifying notable events within the generated gameplay footage.
Innovation Solution
An apparatus and method for video tracking that uses perceptual hashing to detect scene cuts and identify notable events in gameplay, employing computational methods to generate and compare image hashes, and utilize a database of hashes to confirm these events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually review and select notable events from gameplay footage, then recording accuracy and selectivity improve, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs self-service by automatically analyzing gameplay footage, detecting scene cuts, and identifying notable events without requiring manual user review. The automated event detection system processes footage independently, selecting significant moments based on predefined criteria and generating recordings without human intervention.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. Instead of users manually scanning through gameplay footage, the system uses image processing, scene cut detection algorithms, and automated analysis to identify notable events, substituting human operational complexity with automated mechanical processing.
2Reliability
If existing event identification systems are implemented, then notable events can be detected, but system complexity and implementation difficulty increase
Solution Approach 1:
The system segments the complex task of event identification into distinct manageable modules: scene cut detection, notable event identification, and recording generation. Each module handles a specific function independently, making the overall system more manageable and easier to implement while maintaining high reliability through specialized processing at each stage.
Solution Approach 2:
The system performs preliminary actions by first detecting scene cuts and identifying candidate events before finalizing the recording. This staged approach allows the system to pre-process footage, filter potential events, and then generate recordings only for confirmed notable moments, reducing overall system complexity while maintaining identification accuracy.
3Measurement precision
If comprehensive gameplay footage is reviewed to summarize progress, then tracking accuracy improves, but time required for summarization increases
Solution Approach 1:
The system extracts only the essential information needed for progress tracking by identifying and isolating notable events from comprehensive gameplay footage. Instead of reviewing entire hours of gameplay, the system extracts specific scene cuts and notable moments that represent progress, generating summaries that capture accuracy without requiring time-consuming complete footage review.
Data Source
AI summary
A method of identifying notable events within an interactive content includes obtaining corresponding footage of at least two independent instances of the interactive content, and for the footage from each instance, identifying respective changes between successive images, that exceed a predetermined threshold, as scene cuts, and store a data item representative of an image at or near each scene cut, to create data items representing candidate events within the interactive content; then, matching or sequentially aligning the candidate events for each instance, on an overall best-match basis, retaining as review events those candidate events with a match or an aligned match between at least a majority of the candidate events, to create review events, and providing the set or series of review events to a user.