Foldable Camera Switching for Stable Video During Flex Transition
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Solution Overview
Problem
Flexible display devices face challenges such as unstable footage and video discontinuity during flex-state transitions due to difficulties in camera switching and hand movements, leading to interrupted and distorted video recordings.
Innovation Solution
An apparatus and method for seamless video capture involving sensors to identify flex movements, extract semantic scenes, determine regions of interest, forecast device orientation, and switch cameras during transitions using machine learning models to stabilize and compose video frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If camera switching is performed during flex-state transition, then video continuity is improved, but video stability deteriorates due to hand movements and device folding
Solution Approach 1:
The system performs preliminary actions by predicting the flex-state transition before it occurs using sensor data (accelerometer, gyroscope, hinge angle sensors). The machine learning model forecasts the upcoming transition and pre-prepares for camera switching, allowing the system to maintain video continuity while minimizing disruption to video stability by transitioning cameras proactively rather than reactively
Solution Approach 2:
The system introduces an intermediary stabilization process between camera switching and video output. During the transition period, the system captures frames from both source and target cameras, performs semantic scene extraction to identify regions of interest, computes optical flow to determine natural motion, and applies stabilization algorithms to blend frames seamlessly. This intermediary process ensures both video continuity and stability are maintained throughout the transition
2Device complexity
If simple stabilization algorithms are used, then device complexity is reduced, but the ability to address video instability during flex transitions is insufficient
Solution Approach 1:
The stabilization system is segmented into multiple specialized modules: semantic scene extraction module that identifies regions of interest, optical flow computation module that determines natural motion of ROIs, flex trajectory prediction module that forecasts device orientation, and frame stabilization module that applies corrections. This segmentation allows each module to specialize in a specific task, improving overall reliability while managing complexity through modular design
Solution Approach 2:
The system dynamically changes multiple parameters during flex-state transition: it adjusts camera selection based on predicted trajectory, modifies region of interest parameters through semantic scene extraction, alters stabilization strength based on optical flow analysis, and adapts transition timing based on sensor data. These parameter changes enable the system to maintain video stability across varying flex conditions without requiring overly complex algorithms
3Stability of the object's composition
If camera switching is delayed during flex transition, then video stability is maintained, but video discontinuity occurs due to abrupt termination
Solution Approach 1:
The system uses preliminary action by predicting flex-state transitions before they occur using sensor data and machine learning models. This early prediction allows the system to initiate camera switching proactively, ensuring video continuity is maintained while the stabilization modules work in parallel to preserve video stability throughout the transition process
Solution Approach 2:
The system ensures continuity of useful action by capturing video frames from both source and target cameras simultaneously during the transition period. The frame stabilization module continuously processes and blends frames from both cameras, ensuring that video recording continues without interruption or discontinuity while maintaining stability through the transition
Data Source
AI summary
A method for seamless video capture during flex-state transition in a foldable device includes identifying, by one or more sensors of the foldable device, an initiation of a flex movement of the foldable device based on a plurality of frames of a video being captured by a source camera from among one or more cameras of the foldable device; extracting, based on the identifying of the initiation of the flex movement, a semantic scene from the plurality of frames to determine one or more regions of interest (ROIs) in the semantic scene; determining an optical flow for each of the one or more ROIs; determining a flex trajectory of the foldable device; determining a target camera from among the one or more cameras; determining a transition period to switch to the target camera; and switching capturing of the plurality of frames from the source camera to the target camera.