Object Detection Coverage Estimation with Pinhole Camera Geometry
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Solution Overview
Problem
Conventional computer vision systems face challenges in providing accurate real-world spatial coverage information due to system constraint trade-offs, leading to incomplete object detection and inefficient processing.
Innovation Solution
A method and system for object detection coverage estimation that determines spatial coverage of detected objects using camera metadata and a pinhole camera model to calculate three-dimensional coordinates from two-dimensional image data, identifying both coverage areas and blind spots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image resolution is increased (e.g., to 4K) to improve detection accuracy, then object detection accuracy is improved, but computing power requirements increase
Solution Approach 1:
The image processing is divided into multiple passes: first pass processes at lower resolution to identify candidate objects, second pass processes only those candidates at full 4K resolution for accurate detection. This segmentation allows high accuracy for detected objects while reducing overall computing power by avoiding full-resolution processing of entire images.
2Speed
If frame rate is increased to improve real-time detection capability, then response time is improved, but computing power requirements increase
Solution Approach 1:
The system performs partial processing at full resolution only for identified candidate objects rather than processing every pixel at full resolution. This allows maintaining high frame rates by reducing the total computational workload while still providing accurate detection for objects of interest.
3Power
If image resolution is reduced (e.g., from 4K to FHD) to decrease computing power requirements, then computing power requirements are reduced, but object detection accuracy deteriorates
Solution Approach 1:
The system uses a two-stage processing approach where initial candidate identification occurs at reduced resolution, followed by accurate detection at full 4K resolution for only those candidates. This ensures high detection accuracy for detected objects while keeping overall computing power requirements low through selective full-resolution processing.
4Productivity
If conventional object detection methods are used without coverage estimation, then processing speed is improved, but spatial coverage information is lost
Solution Approach 1:
The system performs preliminary object detection and candidate identification before conducting detailed analysis. During this preliminary phase, spatial coverage information is captured and stored, allowing subsequent processing to maintain both speed and complete spatial awareness without requiring additional processing passes.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances decision-making by providing robust and efficient object detection with reduced computing power requirements, improving response time and accuracy by visualizing real-world spatial coverage and blind spots.
Implementation Method 1
calculating the spatial coverage in three-dimensional coordinates of the at least one detected object in response to the two-dimensional image metadata associated with the detected object in the image data acquired by the camera and, further, in response to parameters of the camera using a pinhole camera model
Data Source
AI summary
Methods, systems and computer readable medium for object detection coverage estimation are provided. The system for object detection coverage estimation includes a camera and a processing means. The processing means is coupled to the camera to receive image data acquired by the camera, the image data including a detected object. The processing means is configured to determine a spatial coverage of the detected object based on detected object metadata associated with the detected object in the image data received from the camera.