Electronic Device Camera Composition Prediction and Movement Control
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Solution Overview
Problem
Individuals lack the professional knowledge and skills to shoot high-quality videos, and there are no autonomous robots capable of automatically photographing videos.
Innovation Solution
An electronic apparatus that includes a camera, driver, and processor, which uses neural network models to identify optimal shooting composition and movement paths, adjusting camera settings and positioning to enhance video quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individuals use fixed filming equipment without professional knowledge, then operation simplicity is improved, but video quality deteriorates
Solution Approach 1:
The system enables the camera to automatically adjust its own photographic parameters (shutter speed, aperture, ISO) and positioning without user intervention. The neural network model processes captured images to autonomously determine optimal shooting compositions and movement paths, allowing the device to serve itself rather than requiring user expertise in photography or programming
Solution Approach 2:
The system dynamically changes photographic parameters such as shutter speed, aperture, and ISO based on real-time image analysis by the neural network model. It also changes positional parameters by controlling the camera to move to different locations and angles, thereby adapting to various shooting scenarios and improving video quality while maintaining ease of operation
2Manufacturing precision
If neural network models are used to automatically optimize photographic methods and compositions, then video quality is improved, but device complexity increases
Solution Approach 1:
The neural network model serves multiple functions within the system: it processes captured images, determines optimal shooting compositions, calculates movement paths, and controls both camera parameters and device positioning. This multi-functionality consolidates what would otherwise require multiple separate systems into a single integrated model, managing complexity while improving video quality
Solution Approach 2:
The neural network model acts as an intermediary between the camera's raw captured images and the final optimized video output. It processes the images, determines optimal compositions, and generates control commands for both the camera and device movement, mediating between simple image capture and complex optimization requirements
3Adaptability or versatility
If the camera automatically changes photographic methods and positioning based on captured images, then adaptability is improved, but control complexity increases
Solution Approach 1:
The system continuously captures images, processes them through the neural network model, and uses the results to automatically adjust photographic parameters and device positioning. This closed-loop feedback mechanism enables the system to adapt to changing scenarios in real-time, improving versatility while the automated feedback process manages control complexity
Data Source
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AI summary
An electronic device comprises: a camera; a driver; at least one memory that stores one or more instructions; and one or more processors operatively 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, may cause the electronic device to: acquire an image captured through the camera; and control the driver so as to change, on the basis of an object included in the image, a capturing method of the camera and an arrangement state of the electronic device.