Autonomous Camera Composition Prediction and Movement Control
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Solution Overview
Problem
Existing electronic apparatuses lack the capability to automatically identify and adjust for optimal shooting compositions and movement paths during video recording, resulting in low-quality videos and limited user convenience.
Innovation Solution
An electronic apparatus equipped with a camera, driver, and processors that execute instructions to automatically identify optimal shooting compositions and movement paths by using neural network models to process images and adjust camera settings and placement accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individuals use fixed filming equipment without professional knowledge, then operation simplicity is maintained, but video quality deteriorates
Solution Approach 1:
The electronic apparatus autonomously performs photography by predicting optimal shooting compositions and movement paths without requiring user intervention. The system uses neural network models to automatically determine camera positioning, zoom levels, and movement trajectories, enabling non-professional users to achieve professional-quality videos through autonomous decision-making.
Solution Approach 2:
The system dynamically adjusts multiple camera parameters including position, direction, zoom level, and exposure settings based on real-time image analysis. By continuously optimizing these parameters through neural network predictions, the system transforms fixed equipment into an adaptive system that automatically adjusts to capture optimal compositions.
2Adaptability or versatility
If robots are equipped with cameras but lack automatic photography capability, then device simplicity is maintained, but functionality deteriorates
Solution Approach 1:
The electronic apparatus integrates multiple functions including image capture, neural network-based composition prediction, movement path planning, and autonomous camera control into a single device. This multi-functional integration enables robots and other electronic devices to perform complex photography tasks automatically, enhancing their versatility without requiring separate specialized systems.
Solution Approach 2:
The system replaces manual mechanical control with neural network-based automated control. Instead of requiring physical manipulation of camera equipment, the system uses computational models to predict optimal compositions and control movement, substituting mechanical operations with intelligent decision-making processes.
3Productivity
If manual photography control is used, then user control is maintained, but time consumption increases
Solution Approach 1:
The neural network model predicts optimal shooting compositions and movement paths in advance before actual photography occurs. By pre-calculating the best camera positioning and trajectory based on analyzed images, the system eliminates trial-and-error adjustments and executes photography operations directly according to predictions, significantly reducing time consumption.
Solution Approach 2:
The system continuously monitors captured images and uses this feedback to refine neural network predictions for subsequent photography operations. This iterative feedback mechanism enables real-time optimization of composition and movement, ensuring efficient use of time while improving photography quality through continuous learning and adjustment.
Data Source
AI summary
An electronic apparatus includes: a camera; a driver; at least one memory storing one or more instructions; and one or more processors operably connected to the camera, the driver, and the at least one memory, wherein the one or more instructions, when executed by the one or more processors, cause the electronic apparatus to: obtain an image captured through the camera; and based on an object included in the image, control the driver to change a photographic method of the camera and a placement state of the electronic apparatus.