Focus Adjusting Apparatus Tracking Unpredictable Subjects
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing focus adjusting apparatuses struggle with tracking irregularly moving subjects whose movements are hard to predict, often resulting in tracking failure and requiring user intervention that is difficult for inexperienced camera operators.
Innovation Solution
A focus adjusting apparatus that uses image features to determine the reliability of tracking positions, predicting focus states based on historical data and selecting appropriate areas for focus adjustment, with a determination unit that sets the in-focus area based on reliability thresholds and time periods, facilitating tracking of unpredictable movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prediction based on past focus detection results is used, then tracking precision is improved, but tracking reliability deteriorates when movements cannot be predicted
Solution Approach 1:
The system dynamically switches between two tracking methods based on movement predictability: using prediction-based tracking when movements are predictable and image feature-based tracking when movements are unpredictable. This dynamic adaptation resolves the contradiction by selecting the appropriate method for each situation rather than relying on a single fixed approach.
Solution Approach 2:
The system changes the parameter of tracking method selection based on the predictability of subject movement. When movement patterns are predictable, prediction-based methods are used; when unpredictable, image feature-based methods are used. This parameter change allows the system to maintain both precision and reliability across different tracking scenarios.
2Adaptability or versatility
If user-designated tracking position is used for unpredictable movements, then tracking flexibility is improved, but ease of operation deteriorates due to difficulty in keeping subject at predetermined position
Solution Approach 1:
The system performs self-service by automatically selecting the appropriate tracking position based on image features when movements are unpredictable, eliminating the need for users to manually designate and continuously adjust tracking positions. This resolves the contradiction by making the system adaptable without requiring complex user operations.
Solution Approach 2:
The system performs preliminary action by pre-selecting tracking positions based on image feature analysis before tracking begins, rather than requiring users to continuously adjust positions during operation. This preliminary preparation maintains tracking flexibility while significantly improving ease of operation.
3Reliability
If image features are used to determine tracking position, then tracking reliability is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system applies partial action by using image feature analysis only when prediction-based tracking is insufficient (i.e., when movements are unpredictable). Rather than continuously using image features for all tracking scenarios, the system selectively applies this method only when needed, thereby improving reliability without proportionally increasing complexity.
Solution Approach 2:
Image feature analysis is performed in advance to determine tracking position before focus detection begins, rather than being continuously executed throughout the tracking process. This preliminary action maintains reliability while reducing the overall computational burden and device complexity.
Data Source
AI summary
A focus adjusting apparatus detects a first area of a subject and reliability of a tracking position from an image signal output from an image sensor on which a subject image is formed by an optical system, detects focus states of focus detection areas based on the image signal, determines an in-focus area for which focus adjustment of the optical system is to be performed, predicts a focus state in next focus detection based on a history of focus states of the in-focus area, and selects a second area from the focus detection areas. The first area is determined as the in-focus area in a case where a state in which the reliability is higher than a predetermined first threshold continues for a predetermined time period or longer, and the second area is determined as in-focus area otherwise.