Smart Camera Object Capture Automation
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Solution Overview
Problem
Current digital photography technologies lack the ability to autonomously determine the optimal timing and settings for capturing important objects in a scene, relying heavily on user intervention and failing to utilize camera computing power effectively for decision-making.
Innovation Solution
A method for capturing important objects using a smart camera that involves non-selective image capture, real-time analysis of image sequences to calculate object metrics, automatic decision-making for selective capturing based on predefined criteria, and adjusting camera settings to optimize image quality for objects deemed important, with user input and saliency levels playing a role in determining importance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the camera autonomously determines optimal timing and settings for capturing important objects, then the extent of automation is improved, but the device complexity increases due to real-time analysis and decision-making requirements
Solution Approach 1:
The system segments the image processing task by performing initial non-selective capturing followed by separate analysis of the captured sequence to identify important objects. This divides the complex autonomous decision-making into manageable stages: capture phase, analysis phase, and selective capture phase, reducing overall system complexity while maintaining automation.
Solution Approach 2:
The system performs preliminary non-selective capturing of an image sequence before making autonomous decisions about which objects to capture selectively. This preliminary action provides the necessary data for subsequent analysis and decision-making, enabling automation without requiring complex real-time processing during the capture moment.
2Measurement precision
If real-time analysis of incoming image sequences is performed to calculate object metrics, then the measurement precision of object importance is improved, but the loss of time for processing increases
Solution Approach 1:
The system performs analysis periodically on captured image sequences rather than continuously in real-time. By analyzing the sequence after capture and calculating metrics at intervals, the system achieves precise measurement of object importance while avoiding the time loss associated with continuous real-time processing.
3Productivity
If selective capturing is performed based on calculated metrics, then the productivity of capturing important objects is improved, but the loss of information about non-captured objects increases
Solution Approach 1:
The system performs preliminary non-selective capturing of the entire image sequence before making selective capture decisions. This ensures that complete scene information is initially recorded, and subsequent selective capturing based on metrics enhances productivity by focusing resources on important objects without permanently losing information about other objects.
Solution Approach 2:
The system uses calculated metrics as an intermediary between the captured image sequence and the selective capture decision. These metrics objectively evaluate object importance based on predefined criteria, enabling efficient selective capturing while maintaining the option to recover or analyze non-captured objects through the metric evaluation process.
Data Source
AI summary
A method for capturing important objects with a camera may include the following steps: non-selectively capturing an incoming sequence of images using an image capturing device; analyzing said incoming sequence of images, to yield metrics associated with objects contained within the images, wherein at least one of the metrics is calculated based on two or more images; automatically deciding, in real time, on selectively capturing a new image wherein the selective capturing is carried out differently than the non-selective capturing, whenever the metrics of the objects meet specified criteria; and determining at least one object as an important object, wherein said determining associates the selected object with a level of importance and wherein said metrics are calculated only for the at least one important object.