3D Surveillance Camera Calibration via Point Cloud Embedding
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Solution Overview
Problem
Current 3D surveillance systems face challenges in camera calibration, particularly with dynamic cameras and PTZ motion, as existing methods are either precision-limited or require extensive manual workload, and often fail due to differences between camera images and reference background models.
Innovation Solution
A method and device for interactive calibration based on 3D reconstruction, which involves acquiring a reference background model and surveillance video, capturing overlapping scene images, performing 3D reconstruction to generate a 3D feature point cloud, estimating camera attitude, and calculating homographic transformations to automatically calibrate cameras, thereby embedding image projections into the reference background model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic calibration method based on computer vision is used, then calibration precision is improved, but it requires enough overlapping fields between surveillance images and fails when there are big differences between camera images and reference background model
Solution Approach 1:
The patent introduces a reference image as an intermediary between the surveillance camera image and the 3D background model. The reference image captures the scene at a known reference time and serves as a mediator for feature matching. This allows the system to handle large differences between current surveillance images and the 3D model by comparing against a intermediate reference image that bridges the temporal and spatial gaps.
Solution Approach 2:
The system performs preliminary calibration to establish a correspondence relationship between the reference background model and reference images before processing surveillance videos. This preliminary action creates a foundation that enables subsequent automatic calibration to handle dynamic scenes and PTZ motions more effectively by having pre-established geometric relationships to work from.
2Ease of operation
If interactive calibration method is used, then calibration workload is reduced, but it is only suitable for static cameras and cannot handle camera disturbances and PTZ motion
Solution Approach 1:
The patent transforms the static calibration approach into a dynamic one by introducing temporal dimension through reference images captured at different times. The system can now handle moving cameras and PTZ motions by establishing correspondence relationships across time, allowing the calibration to adapt to dynamic camera positions and orientations while maintaining ease of operation.
Solution Approach 2:
The system uses feedback from feature matching between reference images and surveillance video frames to continuously refine and update the calibration parameters. This feedback mechanism allows the system to automatically adjust to camera movements and disturbances, maintaining accurate calibration without requiring manual re-calibration for each dynamic state.
3Device complexity
If sensor-based calibration method is used, then calibration process is simplified, but precision is limited due to reliance on special equipment
Solution Approach 1:
The patent replaces sensor-based mechanical calibration methods with a vision-based computational approach. Instead of relying on GPS, inertial navigation, or attitude sensors, the system uses computer vision techniques to extract feature points from images and compute calibration parameters through geometric relationships. This substitution eliminates the need for special hardware while achieving higher precision through image-based measurements.
Data Source
AI summary
The present disclosure provides a method and a device for interactive calibration based on 3D reconstruction in a 3D surveillance system. The method includes: acquiring a reference background model and a surveillance video, capturing multiple scene images, and connecting multiple surveillance cameras corresponding to the surveillance video with each other, wherein at least one scene image of the multiple scene images has an overlapping portion with a site of the surveillance video; performing 3D reconstruction based on the multiple scene images to generate a 3D feature point cloud of the scene, and embedding the 3D feature point cloud into the reference background model; estimating an attitude of a current frame of the surveillance video, and automatically calibrating the surveillance camera; and calculating a homographic transformation from the attitude of the current frame to the reference background model, and embedding an image projection into the reference background model.