Dual-Camera Image Processing for Active Region Tracking
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Solution Overview
Problem
Existing systems face inefficiencies and inaccuracies in image acquisition due to the need for manual user intervention when combining wide-angle and dome cameras, leading to poor monitoring efficiency and timeliness.
Innovation Solution
A processor-based system that automatically identifies active regions in a wide-angle camera's view by counting features, clusters these regions, and directs a dome camera to focus on high-feature areas for precise image capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual user intervention is used to set parameters and select regions for the dome camera, then the system can acquire high-resolution images of target objects, but the monitoring efficiency and accuracy deteriorate due to extra operations required and poor timeliness
Solution Approach 1:
The system performs self-service by automatically analyzing the wide-angle image to identify target objects and determine their locations, then autonomously controlling the dome camera to capture high-resolution images without requiring manual user intervention for parameter setting or region selection
Solution Approach 2:
The manual mechanical operation of setting camera parameters and selecting regions is replaced by an automated image processing system that uses computer vision algorithms to detect targets and control the dome camera automatically
2Area of stationary object
If a box camera is used to capture images in a large FOV angle, then the field of view is improved, but the image resolution deteriorates
Solution Approach 1:
The system segments the monitoring task into two stages: first, the box camera captures a wide-area overview image to identify potential target objects; second, the dome camera is directed to capture high-resolution images of the identified targets, thus combining the advantages of both wide coverage and high detail
Solution Approach 2:
The system transitions from a single-dimension approach (one camera type) to a two-dimension approach by combining a wide-angle box camera for area coverage with a high-resolution dome camera for detailed capture, adding the dimension of hierarchical image acquisition
3Measurement precision
If a dome camera is used to capture images with high resolution, then the image quality is improved, but the field of view deteriorates due to smaller FOV angle
Solution Approach 1:
The monitoring task is divided into a wide-area survey phase using the box camera and a detailed inspection phase using the dome camera, allowing each camera to operate in its optimal performance range without compromise
4Productivity
If automatic target detection and region identification is implemented, then monitoring timeliness and efficiency are improved, but the system complexity increases
Solution Approach 1:
The processor is designed to perform multiple functions: it processes images from the box camera, identifies target objects using image processing algorithms, determines optimal regions for capture, and controls the dome camera, thereby reducing the need for separate dedicated components for each function
Data Source
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AI summary
A method for image acquisition may include receiving a video from a first video camera in a period, the video including a first view of a scene. The method may also include delineating a plurality of first regions within the first view of the scene. The method may further include determining an accumulated effect of at least one feature occurring within the period within at least one of the plurality of first regions. The method may further include directing a second video camera towards a second view of the scene based on the accumulated effect.