Camera Focus Control Using Multi-Modal Viewer Attention Fusion
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Solution Overview
Problem
Existing camera focus adjustment techniques fail to account for the attention or interests of the viewer, leading to inconsistent focus on objects at different depths in captured images.
Innovation Solution
A system that utilizes multi-modal sensor fusion to determine appropriate camera focus adjustments based on user attention and interest, incorporating data from various sensors such as eye tracking, environment sensors, and hand detection to adjust focus accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera focus adjustment techniques are used, then the camera can capture objects at certain depths clearly, but objects at other depths (particularly those of viewer interest) may not be in focus
Solution Approach 1:
The system uses eye tracking sensors to detect viewer gaze direction and hand detection sensors to identify objects of interest, providing feedback about viewer attention. This feedback loop enables the camera to dynamically adjust focus to match viewer interest rather than relying on static or depth-based focus algorithms alone
Solution Approach 2:
Multiple sensors (eye tracking, hand detection, depth sensing) act as intermediaries between the viewer and the camera focus system. These sensors capture viewer behavior and translate it into focus adjustment commands, serving as a bridge that enables the camera to understand and respond to viewer interest
2Measurement precision
If multiple sensor-based distance signals are fused to determine focus adjustments, then focus accuracy corresponding to viewer interest is improved, but system complexity increases
Solution Approach 1:
The focus determination process is segmented into distinct functional modules: eye tracking module, hand detection module, depth sensing module, and fusion module. Each module independently processes its specific sensor data and contributes to the overall focus decision, making the complex system more manageable and maintainable
Solution Approach 2:
The sensor fusion system is designed to handle multiple types of distance signals (vergence distance from eye tracking, hand distance from hand detection, object distance from depth sensing) through a unified fusion characteristic framework. This multi-functional approach allows the same fusion mechanism to process diverse sensor inputs without requiring separate processing paths for each sensor type
Data Source
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AI summary
Systems and methods for adjusting camera focus so that objects of interest (e.g., to the image viewer) or objects being attended to (e.g., what the image viewer is attentive to) in the captured camera images are more likely to be in focus in captured images. Information from various sources (e.g., multiple sensors providing information about the user and/or environment) may be fused, e.g., combined or accounted for collectively, to determine how to adjust camera focus in a way that corresponds to viewer interests and/or attention. This may involve determining a fusion characteristic that specifies how to fuse the multiple signals to determine focus adjustments, e.g., selecting or configuring a multi-modal optimization and/or a smoothing function. The fusion characteristic may account for signal confidence. The fusion characteristic may correspond to a determined operational mode used to determine which signals will be used and how the signals will be combined.