Autonomous Camera Self-Learning Triggering
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Solution Overview
Problem
Existing solutions for consumer image capture, such as digital cameras, often require significant operator intervention and setup, limiting the photographer's ability to participate in events and capturing images in a natural and spontaneous manner, especially during fast-moving actions or group events, and fail to provide a seamless combination of still and video capture.
Innovation Solution
An autonomous digital image capture device that configures a learning mode to sense environmental variables, defines a normal state, and initiates image capture when a transition exceeds a predetermined threshold, allowing for automatic triggering of still and video images without direct operator intervention, using sensors and a processor to redefine the normal state based on detected conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If remote camera activation is used for event picture-taking, then the photographer can be freed from behind the camera, but a significant amount of attention and setup is still required
Solution Approach 1:
The camera system performs self-learning by automatically analyzing captured images to identify subjects of interest and trigger conditions without requiring manual programming. The system serves itself by autonomously determining what to capture based on learned patterns from the environment, eliminating the need for complex setup while maintaining photographer freedom.
Solution Approach 2:
The system continuously monitors captured images and uses feedback from image analysis to adjust its triggering behavior. By analyzing the content of captured images and identifying subjects of interest, the system refines its understanding of what constitutes a triggerable event, enabling automatic operation without extensive manual configuration.
2Productivity
If continuous bulk imaging is used, then images can be captured automatically, but excitement, spontaneity, and social interaction are missed
Solution Approach 1:
The system performs preliminary learning by analyzing images to identify subjects of interest and establish trigger conditions before actual event capture begins. This preliminary action enables the system to distinguish between mundane movements and exciting events, capturing only the spontaneous moments worth preserving while maintaining automatic operation.
Solution Approach 2:
The system dynamically adjusts its capture behavior based on real-time analysis of image content and environmental cues. Rather than continuous static capture, the system adapts its triggering sensitivity and subject identification based on the evolving scene, preserving spontaneity while maintaining automatic productivity.
3Ease of operation
If a camera is attached to the photographer, then the user can aim the camera naturally, but the photographer is not brought into the picture
Solution Approach 1:
The system uses environmental cues and subject detection as intermediaries to bridge the photographer and the captured scene. By automatically identifying subjects of interest and triggering capture based on learned conditions, the system mediates between the photographer's presence in the scene and the automatic capture function, eliminating the need for physical attachment while maintaining natural photography experience.
Data Source
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AI summary
The present disclosure relates to an image capture device and a technique for capturing an image. The technique includes configuring an image capture device in a learning mode for sensing at least one variable in the device's surroundings and defining a normal state of the at least one variable. Further, the technique includes initiating image capture upon detecting a transition of the at least one variable from the normal state to a new condition, wherein the transition exceeds a predetermined threshold level, and redefining the normal state to the new condition.