Surveillance Camera Calibration Using Gradient-Descent Height Fitting
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Solution Overview
Problem
Conventional surveillance camera calibration methods fail to achieve accurate apparatus installation parameters due to variations in human average height across different countries or regions, leading to inconsistent calibration accuracy.
Innovation Solution
A calibration method utilizing an image receiver and operation processor to analyze detection images, compute target object heights, and employ error functions and gradient descent to converge on an installation parameter vector, adjusting angles and coordinates for precise calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration methods using human average height are used, then the calibration process is simple, but the calibration accuracy deteriorates due to regional variations in height
Solution Approach 1:
The patent changes the calibration parameter from using human average height (which varies by region) to using a standardized calibration object with fixed, known dimensions. This parameter change eliminates regional variations and improves measurement precision while maintaining a relatively simple calibration process.
Solution Approach 2:
The patent introduces a virtual calibration object (digital model) with precisely defined geometric parameters that can be overlaid on the detection image. This virtual copy provides a reference standard that is consistent across all regions, improving calibration accuracy without requiring physical calibration objects or complex regional adjustments.
2Measurement precision
If standardized calibration objects are used instead of human average height, then calibration accuracy improves, but the ease of operation deteriorates due to additional computational steps
Solution Approach 1:
The calibration system performs automatic calibration by acquiring detection images, extracting feature points, and computing installation parameters through gradient descent optimization without requiring manual intervention. The system serves itself by using the detection image data to automatically determine the calibration parameters, improving ease of operation despite the increased computational complexity.
Solution Approach 2:
The patent implements an iterative optimization process using gradient descent that continuously refines the installation parameters by minimizing the error between the virtual calibration object and the actual detection image. This feedback mechanism automates the calibration process, making it easier to operate while maintaining high accuracy through multiple verification steps.
Data Source
AI summary
A calibration method of apparatus installation parameter is applied to a surveillance device having an image receiver and an operation processor. The calibration method includes analyzing a detection image from the image receiver to acquire an apparatus installation parameter of the surveillance device, computing a height of at least one target object inside the detection image in accordance with the apparatus installation parameter, analyzing the height of the at least one target object via an error function to acquire an error computed value, computing a local minimal value of the error computed value relevant to the apparatus installation parameter via a gradient descent function, and finding the local minimal value to acquire an installation parameter vector so that the surveillance device executes analysis of the detection image according to the installation parameter vector.