Voice Controlled Camera AI Scene Detection Focusing
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Solution Overview
Problem
Amateur photographers face challenges in selecting appropriate camera settings for precise focusing, especially when subjects move or are part of a complex scene, as conventional touch-screen focusing methods are limited and prone to losing track of moving objects.
Innovation Solution
A voice-controlled camera with AI scene detection that uses natural language processing to understand voice commands, generates depth maps, and adjusts camera settings such as focus points, aperture, and shutter speed to ensure precise focusing on desired subjects within a scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If touch-screen focusing method is used, then ease of operation is improved, but reliability deteriorates when objects move or rotate too much causing tracking loss
Solution Approach 1:
The patent replaces the mechanical touch-screen interaction system with a voice control system using natural language processing. The microphone captures voice commands, which are then processed by NLP algorithms to interpret user intent and translate into camera control actions, eliminating the need for physical touch interaction while maintaining ease of use.
Solution Approach 2:
The patent introduces an intermediary layer between user input and camera control through the use of natural language processing and voice recognition algorithms. This intermediary translates spoken commands into meaningful camera operations, providing more robust tracking capability while maintaining user-friendly operation.
2Device complexity
If conventional voice control methods with direct mapping are used, then device complexity is reduced, but adaptability deteriorates as complex camera tasks cannot be executed
Solution Approach 1:
The patent implements a universal voice control system that can handle multiple types of camera tasks through natural language processing. A single voice interface can perform diverse functions including focusing on specific objects, adjusting camera settings, and executing complex photography commands, making the system highly adaptable without requiring separate controls for each function.
Solution Approach 2:
The patent utilizes parameter changes in the voice recognition system by processing natural language inputs through NLP algorithms that can interpret various command structures and translate them into appropriate camera parameters and settings, enabling versatile control while maintaining relatively simple device architecture.
3Manufacturing precision
If expert camera settings selection is required, then manufacturing precision is improved for focus accuracy, but ease of operation deteriorates due to complicated menus and buttons
Solution Approach 1:
The patent implements a self-service system where the camera automatically analyzes the captured image, identifies the subject, and adjusts focus settings without requiring user intervention with complex menus. The system uses AI algorithms to autonomously determine optimal focus parameters based on the detected subject, providing expert-level precision while maintaining simple operation.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the captured image through AI scene detection and subject identification algorithms before the user needs to take the photograph. The system proactively analyzes the image content, determines the appropriate focus point, and prepares the camera settings in advance, eliminating the need for users to manually navigate complex focus menus.
Data Source
AI summary
An apparatus, method and computer readable medium for a voice-controlled camera with artificial intelligence (AI) for precise focusing. The method includes receiving, by the camera, natural language instructions from a user for focusing the camera to achieve a desired photograph. The natural language instructions are processed using natural language processing techniques to enable the camera to understand the instructions. A preview image of a user desired scene is captured by the camera. Artificial Intelligence (AI) is applied to the preview image to obtain context and to detect objects within the preview image. A depth map of the preview image is generated to obtain distances from the detected objects in the preview image to the camera. It is determined whether the detected objects in the image match the natural language instructions from the user.