Deep Learning Video Editing for Automated Object-Based Segment Selection
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Solution Overview
Problem
Existing video editing methods require significant labor and time investments due to the need for users to manually segment and splice video content, leading to low efficiency.
Innovation Solution
A deep learning-based video editing method that utilizes a deep learning model to perform attribute recognition on objects within a target video, selecting relevant segments or frames based on editing requirements, and automatically generating an edited video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual video editing is performed by users watching and segmenting video content, then editing accuracy can be maintained, but time consumption and labor costs increase significantly
Solution Approach 1:
The patent replaces the manual mechanical editing process with an automated deep learning-based system. The deep learning model automatically performs attribute recognition on video objects and generates edited videos without manual intervention, substituting human mechanical operations with an automated computational system that maintains editing quality while dramatically reducing time consumption
Solution Approach 2:
The system enables self-service video editing through automated attribute recognition and intelligent segment selection. The deep learning model autonomously analyzes video content, identifies key objects and attributes, and generates edited videos without requiring user interaction, allowing the system to serve itself in the editing process
2Manufacturing precision
If users manually segment and splice video content after watching the entire video, then editing quality can be ensured, but labor costs and time investment increase
Solution Approach 1:
The patent replaces manual video segmentation and splicing operations with an automated deep learning system. The model performs attribute recognition on video frames, automatically identifies segments meeting editing requirements, and assembles the final video without manual mechanical editing operations, thereby maintaining quality while improving productivity
Solution Approach 2:
The system performs preliminary attribute recognition and analysis on the entire video content before segmentation. The deep learning model pre-processes the video by identifying objects, attributes, and potential segments in advance, which enables efficient and accurate editing without requiring users to watch and analyze the video manually before editing
3Productivity
If automatic video editing is implemented using deep learning, then time and labor costs are reduced, but system complexity increases
Solution Approach 1:
The patent introduces a deep learning model as an intermediary between the raw video content and the final edited output. This intermediary component performs attribute recognition and intelligent decision-making, bridging the gap between automated processing and high-quality results while managing system complexity through a specialized intermediate layer
Data Source
AI summary
A deep learning-based video editing method can allow for automated editing of a video, reducing or eliminating user input, saving time and labor investments, and thereby improving video editing efficiency. Attribute recognition is performed on an object in a target video using a deep learning model. A target object is selected that satisfies an editing requirement of the target video. A plurality of groups of pictures associated with the target object from the target video are obtained using editing. An edited video corresponding to the target video is generated using the plurality of groups of pictures.