Object Detection in Distorted Surveillance Images
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Solution Overview
Problem
Existing video surveillance systems face challenges in detecting and tracking objects across multiple cameras due to radial and projective distortion, which reduces the reliability of object detection and descriptor extraction, and causes operator fatigue from interpreting distorted images.
Innovation Solution
A method that detects a part of an object in a distorted image, predicts the location of another part on an undistorted image plane using calibration parameters, and determines the object by transforming the detected locations back to the distorted image, allowing for the generation of a rectified image without requiring knowledge of camera location or assuming a planar ground.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radial and projective distortion correction is applied to improve object detection accuracy, then measurement precision improves, but device complexity increases due to required calibration parameters and transformation computations
Solution Approach 1:
The system performs self-calibration by automatically determining distortion parameters from the image data itself without requiring external calibration equipment or manual parameter input. The calibration parameters are derived from the distorted image through automated detection of known patterns or features, enabling the system to correct its own distortions independently.
Solution Approach 2:
Instead of using expensive, precise calibration targets or equipment, the system uses readily available image data and computational algorithms to achieve distortion correction. The method relies on software-based transformations rather than hardware modifications, reducing overall system cost and complexity.
2Ease of operation
If full image rectification is applied to remove distortion, then operator fatigue is reduced, but processing time increases
Solution Approach 1:
Instead of rectifying the entire image, the system extracts only the relevant object regions and applies distortion correction selectively to these areas. This extraction approach maintains operator comfort by presenting corrected views while significantly reducing the computational burden compared to full-image processing.
Solution Approach 2:
The system applies partial rectification only to the extent necessary for accurate object detection and display. Rather than performing complete geometric transformation of the entire image frame, it focuses computational resources on correcting distortion in regions containing objects of interest, achieving sufficient correction with less processing.
3Manufacturing precision
If camera location and orientation knowledge is required for distortion correction, then manufacturing precision improves, but adaptability decreases
Solution Approach 1:
The system performs preliminary calibration by automatically determining camera parameters and distortion characteristics from the image data before object detection begins. This preliminary action establishes the necessary correction parameters without requiring pre-installation calibration or external knowledge of camera positioning, enabling the system to adapt to different camera configurations automatically.
Solution Approach 2:
The system dynamically determines and adjusts distortion correction parameters based on the specific image data being processed. Rather than relying on fixed, pre-configured camera parameters, it computes appropriate transformation parameters for each imaging scenario, allowing the system to adapt to varying camera locations, orientations, and distortion characteristics.
Data Source
AI summary
A system and method for determining an object in a distorted image. The method comprises detecting a location of a first part of the object on the distorted image; determining a corresponding location of the first part of the object on an undistorted image plane corresponding to the distorted image based on a plurality of calibration parameters; predicting a location of a second part of the object on the undistorted image plane based on the determined location of the first part on the undistorted image plane; determining a corresponding location of the second part of the object on the distorted image based on the predicted location of the second part of the object on the undistorted image plane and the calibration parameters; and determining the object based on the detected location of the first part of the object and the determined location of the second part in the distorted image.