Video Curation System Using ML Object Detection for Clip Selection
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Solution Overview
Problem
Current video recording technologies generate overwhelming amounts of unedited content, making it difficult for users to efficiently share relevant moments on social media, as existing automated video editing systems struggle to accurately select and clip interesting content without user intervention.
Innovation Solution
A video curation system that employs machine learning algorithms to identify and clip relevant moments in real-time video recordings based on user input, object recognition, and contextual triggers, allowing for automatic generation and sharing of short-form video clips.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automated video editing software is used to highlight moments in recorded videos, then the burden of reviewing countless hours of recorded video is reduced, but the system still struggles to accurately select and clip interesting content without user intervention
Solution Approach 1:
The patent introduces an intermediary system that bridges automatic video analysis and user selection. The system automatically generates candidate clips based on detected events and metadata, then presents these curated candidates to the user for selection, rather than requiring the user to review all raw footage or relying on completely automatic selection.
Solution Approach 2:
The system implements feedback loops where user selections and corrections are used to refine and improve automatic clip generation. The system learns from user interactions with automatically generated clips, adjusting its event detection and clipping algorithms to better match user preferences over time.
2Quantity of substance
If long, unedited content streams are shared on social networking sites, then all recorded content is available for viewing, but the content is overwhelming to a viewer
Solution Approach 1:
The patent divides long, continuous video recordings into discrete, meaningful segments or clips based on detected events, actions, or changes in the scene. Each segment represents a specific moment or event of interest, making the content more manageable and engaging for social media viewers while preserving the essential information from the original recording.
Solution Approach 2:
The system extracts key moments and interesting segments from long video recordings, separating these highlight clips from the bulk of the unedited footage. This extraction process identifies and isolates the most valuable content portions for sharing, eliminating the need to share entire hours of recording.
3Quantity of substance
If motion detection is used to limit the amount of video recording data, then storage costs are reduced, but the system may miss subtle or complex events
Solution Approach 1:
The patent replaces traditional motion detection mechanisms with more advanced analysis methods including computer vision, object recognition, and metadata analysis. These systems can detect subtle events and complex patterns that simple motion thresholds would miss, while still maintaining efficient storage by only recording or flagging moments when events are detected.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a device that includes a processing system with a processor, and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations such as receiving user input comprising a keyword identifying an object, monitoring a video recording during a generation of the video recording by a camera, wherein the monitoring includes detecting the object being captured by the camera, creating a video clip from the video recording, wherein the video clip comprises a start point and a stop point in the video recording determined by a machine learning algorithm, and sending a notification of the creating of the video clip. Other embodiments are disclosed.