Camera Calibration Using Person Bounding Boxes
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Solution Overview
Problem
Existing camera calibration methods for video surveillance systems are inefficient, often requiring user interaction or relying on deep learning, which can be computationally intensive and prone to noise, especially when estimating camera parameters using detected objects or man-made structures.
Innovation Solution
A method that uses projective geometry to calibrate cameras by estimating extrinsic parameters such as tilt, roll, and height based on multiple detections of persons within bounding boxes, employing a feet-to-head homology and image-to-ground homography to map foot vertices to head vertices, thereby iteratively updating camera parameters for accurate calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used to estimate camera parameters from detected objects, then measurement precision may be improved, but device complexity and computational resources increase significantly
Solution Approach 1:
The patent extracts only the essential geometric information (bounding boxes of detected persons) from the image data, discarding the need for complex deep learning models. By taking out only the necessary information (object locations and sizes) and using simple geometric relationships to derive camera parameters, the system achieves accurate calibration without deep learning complexity
Solution Approach 2:
The patent replaces the complex 'mechanical system' of deep learning computations with a geometric/mathematical approach using vanishing points, horizon lines, and perspective projection geometry. This substitution uses fundamental projective geometry principles instead of neural networks, significantly reducing computational requirements while maintaining accuracy
2Measurement precision
If deep learning methods are used for camera calibration, then measurement precision may be improved, but loss of energy and computational resources increase
Solution Approach 1:
The patent uses simple, computationally inexpensive geometric calculations instead of expensive deep learning models. The bounding boxes and basic projective geometry operations are 'cheap' in terms of computational resources, providing sufficient accuracy without the high energy cost of training and running neural networks for calibration
3Ease of operation
If user interaction is required for camera calibration, then ease of operation may be improved, but productivity decreases
Solution Approach 1:
The system performs calibration automatically using detected persons in the scene without requiring user interaction. The algorithm self-calibrates by detecting bounding boxes of persons and computing camera parameters from their geometric relationships, eliminating the need for manual calibration procedures while maintaining ease of use
Solution Approach 2:
The system prepares for calibration by automatically detecting and localizing persons in the scene before performing the actual parameter estimation. This preliminary detection phase sets up the geometric relationships needed for calibration without requiring user intervention at any stage
4Measurement precision
If extensive calibration data is collected, then measurement precision is improved, but loss of time and productivity decrease
Solution Approach 1:
The patent achieves sufficient calibration accuracy using a limited number of detected persons rather than requiring extensive calibration data. By using the geometric relationships from a small number of bounding boxes, the system obtains adequate precision without the time cost of collecting and processing large datasets
Data Source
AI summary
Methods, systems, and techniques for automatic camera calibration. One or more calibration images are captured using a camera. The calibration images depict one or more bounding boxes, and each of the bounding boxes bounds a person. For each of the bounding boxes, the person is modeled using a rectangle or a parallelepiped, and a projection of the rectangle or parallelepiped is determined. A mapping that maps foot vertices of the projection to head vertices of the projection is determined, and using the foot vertices and the mapping, estimates of the head vertices and distances between the head vertices and the estimates of the head vertices are determined. The camera is calibrated by iteratively updating, using an objective function, the camera parameters so as to reduce those distances.