Video Camera Privacy Masking via Georeferenced Coordinates
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Solution Overview
Problem
Current video surveillance systems require manual definition of privacy masks, which is inefficient and does not automatically adapt to changing monitoring areas, failing to effectively protect privacy in public spaces.
Innovation Solution
A method and device that use georeferenced coordinates from maps, such as Google Map API, to automatically determine and set the monitored area by a video camera, calculating a mask to gray out or remove contents outside the defined area of interest, based on the camera's 3D pose and viewing rays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual definition of privacy masks is used, then privacy protection is achieved, but installation time and complexity increase
Solution Approach 1:
The system automatically determines the monitored area and generates privacy masks using geo-referenced coordinates and computer vision algorithms, eliminating the need for manual configuration. The camera system self-configures by detecting edges and contours in the video stream to automatically define monitoring boundaries and apply masking to protected areas.
Solution Approach 2:
The system pre-defines geo-referenced coordinate systems and area boundaries before actual monitoring begins. By establishing the geographic reference framework in advance, the system enables automatic mask generation without requiring manual intervention during installation or operation.
2Reliability
If manual definition of privacy masks is used, then privacy protection is achieved, but adaptability to changing areas is reduced
Solution Approach 1:
The system dynamically adapts to changing monitoring areas by continuously using geo-referenced coordinates and real-time video analysis. When monitoring areas change, the system automatically recalculates the appropriate privacy masks based on the new geographic boundaries and camera position, maintaining privacy protection without manual reconfiguration.
Solution Approach 2:
The system changes parameters such as camera position, orientation, and geo-referenced coordinate values to adapt to different monitoring scenarios. By modifying these parameters, the system automatically adjusts the privacy mask boundaries and areas to match the new monitoring requirements while maintaining protection of sensitive zones.
3Productivity
If automatic mask determination using geo-referenced coordinates is implemented, then installation efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces geo-referenced coordinate systems as an intermediary layer between the physical camera installation and the digital mask generation. This intermediary framework standardizes the relationship between camera position and monitoring area, enabling automatic mask determination through algorithmic processing rather than manual configuration, thus improving installation efficiency.
Solution Approach 2:
The system replaces manual mechanical configuration processes with automated computational methods. Instead of manually positioning and configuring cameras and masks, the system uses geo-referenced coordinates, computer vision algorithms, and automated processing to determine monitoring areas and generate privacy masks, significantly improving installation efficiency.
4Reliability
If broader monitoring areas are covered, then surveillance effectiveness is improved, but privacy violations increase
Solution Approach 1:
The system applies different quality treatments to different spatial zones within the monitoring area. Sensitive areas such as private properties or restricted zones are identified using geo-referenced coordinates and automatically masked, while public or permitted areas maintain full surveillance coverage. This local differentiation allows effective surveillance where appropriate while protecting privacy where required.
Solution Approach 2:
The monitoring area is segmented into distinct zones based on geo-referenced coordinates and privacy requirements. The system divides the field of view into monitored areas and protected areas, applying different processing treatments to each segment. This segmentation enables simultaneous achievement of comprehensive surveillance effectiveness and targeted privacy protection.
Data Source
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AI summary
The invention relates to a method for determining and setting an area to be monitored by a video camera, wherein a field of vision detected by the camera that results from the installation and calibration of the camera is reduced to the area to be monitored in that the position and orientation of the camera are described by means of georeferenced coordinates, which are taken from a georeferenced map of the area to be monitored, and a mask is automatically determined using the georeferenced coordinates, which mask masks contents of the field of vision outside of the area to be monitored during the mapping onto the field of vision. The invention further relates to a corresponding device and a corresponding camera assembly.