Video Generating Apparatus for Automatic Content Event Extraction
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Solution Overview
Problem
The cost and time constraints for ordinary streamers to record and edit videos from live-streaming platforms for uploading to non-live streaming platforms are significant, due to limited investment capital and rudimentary editing skills, making it difficult to extract and generate specific video sections efficiently.
Innovation Solution
A method and system that utilize an inference model to extract frame images from a target video, generating shorter videos by selecting specific sections based on content event information, allowing for automatic video recording and editing of desired sections, reducing the need for full video editing and storage of all frame images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all frame images are recorded and stored for later editing, then complete video content is preserved, but storage cost and time consumption increase significantly
Solution Approach 1:
The patent extracts only the necessary frame images from the complete video stream by using an inference model to identify content events. Instead of storing all frames, the system extracts and stores only the frames corresponding to identified events, significantly reducing storage requirements while preserving important video content.
Solution Approach 2:
The patent segments the video into discrete content events based on inference model analysis. The video stream is divided into meaningful segments (content events) that can be individually processed and stored, allowing selective storage of only the significant portions rather than the entire video.
2Manufacturing precision
If manual editing of recorded videos is performed, then video quality and relevance are improved, but time consumption and skill requirements increase
Solution Approach 1:
The patent implements self-service by using the inference model to automatically identify content events and determine which frames should be extracted and edited. The system performs the editing decision-making process autonomously based on the video content, eliminating the need for manual analysis and selection by the streamer.
Solution Approach 2:
The inference model provides feedback about the video content by identifying content events in real-time or near-real-time. This feedback information guides the automatic extraction and editing processes, ensuring that the most relevant content is selected without requiring manual review and adjustment.
3Measurement precision
If inference models process all frame images, then accurate content event identification is achieved, but processing time and computational cost increase
Solution Approach 1:
The patent applies partial action by processing only the frame images that are likely to contain content events, rather than processing every frame. The inference model selectively analyzes frames based on video context and event detection algorithms, achieving accurate content event identification while reducing overall processing time and computational resources required.
Data Source
AI summary
Systems and methods for easily extracting images of sections from existing video and generating sub-videos are illustrated. One embodiment includes a video generating method including obtaining a target video including a plurality of frame images, wherein the target video is transmitted through a session established between two or more terminals, obtaining first frame information about at least some frame images, from among the plurality of frame images, selected to be inference targets, inputting the at least some frame images corresponding to the first frame information into an inference model to obtain content event information for extracting at least one extracted video with respect to the target video, obtaining information about a selected section of the target video based on the content event information, and generating a first extracted video based on the target video and information about the selected section.