Camera Focus Stabilization via Semantic Region Analysis
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Solution Overview
Problem
In dynamic environments like transportation and fulfillment centers, determining which portions of a video camera's field of view to keep in focus is challenging due to varying sizes, shapes, and velocities of people, objects, and machines, especially when the camera has a fixed orientation and captures large numbers of diverse entities.
Innovation Solution
The system identifies semantically related image pixels or objects within the field of view, specifies them as regions of interest, and automatically adjusts the camera to place these regions within an appropriate depth of field for clear imaging, using probabilistic analyses and feedback loops to determine optimal focal lengths based on blur metrics and perceptual scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the camera captures a wide field of view to monitor diverse entities, then the coverage area is improved, but the ability to maintain focus on specific regions deteriorates
Solution Approach 1:
The patent divides the field of view into multiple depth layers (foreground, midground, background) and independently controls focus for each layer. This segmentation allows the camera to maintain focus on specific regions of interest while preserving a wide field of view for monitoring diverse entities at different distances.
Solution Approach 2:
The patent implements dynamic focus adjustment by continuously analyzing the field of view to identify regions of interest and automatically adjusting the focal length to keep these regions within the depth of field. This dynamic adaptation enables the system to maintain focus accuracy on moving or varying targets while preserving comprehensive scene coverage.
2Device complexity
If the camera uses a fixed orientation to simplify positioning, then the installation complexity is reduced, but the ability to track moving objects deteriorates
Solution Approach 1:
The patent replaces mechanical camera movement with computational image processing and focus adjustment. Instead of physically repositioning or reorienting the camera to track objects, the system uses algorithms to identify moving objects in the fixed field of view and adjusts the focal length to maintain focus on these objects, thereby achieving tracking capability without mechanical complexity.
Solution Approach 2:
The patent changes the focal length parameter dynamically based on detected object positions and depths. By adjusting this optical parameter, the system adapts to track moving objects and maintain focus on regions of interest while the camera remains physically fixed, thus achieving versatility without increasing positioning complexity.
3Quantity of substance
If the camera monitors multiple diverse entities simultaneously, then the monitoring coverage is improved, but the difficulty of determining focus regions increases
Solution Approach 1:
The patent implements a feedback loop where the system continuously analyzes the field of view to identify regions of interest, adjusts the focal length accordingly, and then evaluates the resulting image quality. This feedback mechanism enables automatic focus optimization on multiple diverse entities simultaneously by using image sharpness metrics to guide focus adjustments, reducing the difficulty of focus region identification.
Solution Approach 2:
The patent performs preliminary analysis of the field of view to identify potential regions of interest before adjusting focus. By pre-processing the image data to detect edges, contours, and semantic features, the system prepares focus region candidates in advance, making the subsequent focus adjustment more efficient and accurate when monitoring multiple diverse entities.
Data Source
AI summary
An imaging device may be configured to monitor a field of view for various objects or events occurring therein. The imaging device may capture a plurality of images at various focal lengths, identify a region of interest including one or more semantic objects therein, and determine measures of the levels of blur or sharpness within the regions of interest of the images. Based on their respective focal lengths and measures of their respective levels of blur or sharpness, a focal length for capturing subsequent images with sufficient clarity may be predicted. The imaging device may be adjusted to capture images at the predicted focal length, and such images may be captured. Feedback for further adjustments to the imaging device may be identified by determining measures of the levels of blur or sharpness within the subsequently captured images.