Video Segmentation via Rotational Motion Metadata
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Solution Overview
Problem
Electronic devices struggle to effectively segment and analyze video metadata based on rotational motion and angle changes during video capture, which affects video editing and processing efficiency.
Innovation Solution
Incorporating sensors and processors in electronic devices to identify angles and rotational motions, allowing for the segmentation of video portions based on designated ranges, and obtaining metadata for time intervals where these changes occur, enabling improved video editing and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors and processors are incorporated to identify angles and rotational motions during video capture, then video segmentation accuracy and metadata quality are improved, but device complexity increases
Solution Approach 1:
The sensor system is designed to perform multiple functions: detecting rotational motion, measuring angle changes between housing portions, and identifying temporal patterns. This multi-functionality allows the same hardware infrastructure to support video segmentation without requiring separate dedicated components for each measurement type, thereby improving measurement precision while limiting the increase in device complexity.
Solution Approach 2:
The system performs preliminary detection of rotational motion and angle changes during video capture, storing this metadata information in advance. This preliminary action enables subsequent video segmentation to be performed more accurately without requiring complex real-time processing during playback, thus improving segmentation accuracy while managing device complexity through pre-computation.
2Productivity
If video segmentation is performed based on rotational motion and angle changes, then video editing efficiency is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of rotational motion and angle changes during video capture, pre-identifying potential segmentation points and storing associated metadata. This preliminary action reduces the computational burden during actual video editing operations, as the segmentation decisions are based on pre-processed data rather than requiring intensive real-time analysis, thus improving editing efficiency while limiting additional processing time.
Solution Approach 2:
The video processing system automatically generates segmentation metadata and identifies editing points without requiring manual intervention or complex external processing. This self-service capability allows the system to improve video editing efficiency by autonomously performing segmentation based on detected motion patterns, reducing both processing time and computational resource requirements compared to manual or externally-intensive methods.
Data Source
AI summary
An electronic device includes: a first housing; a second housing; a first sensor; a second sensor; at least one camera provided on the first housing; and a processor configured to: identify an angle between the first housing and the second housing, in a state of obtaining a video by controlling the at least one camera; identify a magnitude of a rotational motion of the electronic device, in the state of obtaining the video; and obtain information for segmenting a portion of the video, which corresponds to a time interval in which at least one of the identified angle being changing by exceeding a designated range or the identified magnitude of the rotational motion being exceeding a designated magnitude.