Georeferencing Camera Model Using Lookup Tables for Video Surveillance
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Solution Overview
Problem
Current video surveillance systems lack efficient georeferencing capabilities, particularly with single cameras in landmark-deficient scenes, which hinders accurate object size determination and kinematic analysis, leading to reliance on human intervention and increased costs.
Innovation Solution
A method for constructing a georeferencing-enabled camera model using a single PTZ camera, which derives camera models from principal and auxiliary views with landmarks, translating optical nonlinearities into lookup tables to map image-pixel coordinates to three-dimensional terrain points, enabling georeferencing of targets in video sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single camera is used for video surveillance, then device complexity and cost are reduced, but georeferencing precision deteriorates due to insufficient landmark dispersion in the scene
Solution Approach 1:
The system performs preliminary calibration by capturing images of the scene at multiple known camera positions and orientations. Landmarks are identified and their coordinates are stored in advance, creating a lookup table that maps image coordinates to real-world coordinates. This preliminary action enables accurate georeferencing during actual surveillance without requiring landmarks to be visible in every subsequent view.
Solution Approach 2:
The system creates a virtual model of the scene by copying and storing the spatial relationships between landmarks observed during calibration. This virtual model, stored as coordinate mappings and lookup tables, allows the system to georeference targets in new views by referencing the pre-established coordinate system, effectively copying the geospatial structure without requiring physical landmarks in every view.
2Measurement precision
If landmarks are introduced into the scene to improve georeferencing, then measurement precision improves, but ease of operation deteriorates due to the need for scene modification
Solution Approach 1:
The system performs self-calibration by automatically identifying landmarks in the scene and computing their coordinates without requiring manual placement or modification of the environment. The calibration process uses the camera's own motion and the natural features already present in the scene, eliminating the need for external intervention to add physical landmarks.
Solution Approach 2:
The system changes the parameter of camera position and orientation during calibration, capturing images from multiple viewpoints. By varying these parameters and establishing coordinate mappings across different views, the system achieves accurate georeferencing without adding physical landmarks to the scene.
3Measurement precision
If multiple cameras are deployed to achieve adequate landmark dispersion, then georeferencing precision improves, but device complexity and cost increase
Solution Approach 1:
The system uses a dynamic single camera that can be positioned and oriented at multiple locations during calibration. By making the camera mobile and capturing images from various positions and angles, the system achieves the equivalent georeferencing precision that would require multiple fixed cameras, but with reduced system complexity and cost.
4Reliability
If manual monitoring by human personnel is used, then detection reliability improves, but productivity decreases due to labor-intensive operation
Solution Approach 1:
The system provides automated feedback by continuously analyzing video frames, detecting targets, and georeferencing them using the pre-established coordinate mappings. This closed-loop automated process maintains detection reliability while eliminating manual labor, allowing simultaneous monitoring of multiple camera feeds and immediate alerting when threats are detected.
Data Source
AI summary
A method for constructing a georeferencing-enabled camera model and its deployment in georeferencing targets located in video sequences due to a single video camera. A video surveillance camera is modeled by a collection of rays converging at a virtual camera point and the retina resolution cell coordinates associated with those rays wherein the ray equations are first established, in the course of a calibration process, for a given camera view, with the aid of other views of the same video surveillance camera, as necessary, and using such a model for mapping image coordinates to terrain coordinates and vice versa in the intended view or its adaptation for use in other views of the same video surveillance camera.