Camera Region Segmentation for Privacy Protection
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Solution Overview
Problem
Security cameras often capture and output image data that includes private property beyond their owner's field of view, potentially violating terms of service and creating legal liabilities due to invasion of privacy concerns.
Innovation Solution
A camera system that detects objects and activities within specific regions of interest, selectively outputs image data based on region labels such as 'private property,' allowing for the blurring or blackout of sensitive areas to prevent unauthorized data capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the camera system captures and outputs image data from the entire field of view, then the monitoring coverage is maximized, but legal liabilities and privacy violations occur due to capturing private property beyond the owner's property
Solution Approach 1:
The camera system divides the field of view into multiple labeled regions (e.g., owner's property, neighbor's property, public property) and applies different output settings to each region. This segmentation allows the system to maximize monitoring coverage of the owner's property while automatically excluding or obscuring private property areas, thereby resolving the contradiction between coverage area and privacy violation risk.
Solution Approach 2:
The system applies different quality characteristics to different regions of the captured image data. Regions labeled as private property receive different treatment (such as blurring, blacking out, or exclusion) compared to regions labeled as owner's property. This local differentiation enables the system to maintain high-quality monitoring of authorized areas while protecting privacy in unauthorized areas, thus resolving the contradiction.
2Object-affected harmful factors
If the camera system selectively outputs image data based on region labels, then privacy violations are prevented, but the complexity of the system increases due to region detection and labeling requirements
Solution Approach 1:
The camera system automatically performs region detection, labeling, and selective output configuration without requiring manual intervention. The system services itself by autonomously identifying property boundaries, assigning labels based on predefined criteria, and applying appropriate output settings. This self-service approach reduces the operational complexity burden on users while maintaining the privacy protection functionality.
Solution Approach 2:
The system performs preliminary region detection and labeling during the image capture process itself, rather than as a separate post-processing step. By integrating region identification and labeling into the primary image processing pipeline, the system avoids adding significant complexity while enabling selective output based on region characteristics.
3Object-affected harmful factors
If the camera system blurs or blacks out sensitive areas, then unauthorized data capture is prevented, but the loss of information occurs in those regions
Solution Approach 1:
The system extracts and separates the private property regions from the overall image data stream. Instead of obscuring information within the complete image, the system identifies and extracts only the portions corresponding to private property, applying different output settings solely to these extracted regions. This approach prevents unauthorized capture of private property while preserving complete information in the owner's property regions, thus resolving the contradiction between preventing unauthorized capture and maintaining information integrity.
Data Source
AI summary
A camera system may capture object activity with a field of view comprising one or more regions and identify and selectively output captured image data based on the one or more regions.