Anonymizing Visual Features Using Sorting Algorithms
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Solution Overview
Problem
The manual process of anonymizing features in video and photo surveillance images is time-consuming and inefficient, requiring manual intervention to obscure identifying elements such as faces, locations, and brands.
Innovation Solution
A method and system that utilize a sorting algorithm to identify and classify features in images, applying predetermined obscuring annotations to anonymize specific categories, such as faces, text, and locations, through image preprocessing and filtering, and modifying the images using techniques like pixilation, blurring, and morphological operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual anonymization process is used, then identifying features can be obscured to protect privacy, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces the manual mechanical process of anonymization with an automated computer vision system using deep learning algorithms. The system automatically detects, classifies, and obscures identifying features such as faces, license plates, and logos through algorithmic processing, eliminating the need for manual intervention while maintaining privacy protection effectiveness.
Solution Approach 2:
The anonymization system performs self-service by automatically detecting and processing identifying features without human intervention. The deep learning model autonomously identifies relevant features, determines appropriate obscuration methods, and applies them consistently across images, making the system self-sufficient and highly efficient.
2Reliability
If manual anonymization is performed, then identifying features can be obscured, but human intervention is required which reduces efficiency
Solution Approach 1:
The patent replaces manual human operations with an automated computer vision system. The system uses deep learning algorithms to automatically detect, classify, and obscure identifying features, completely eliminating the need for human intervention in the anonymization process while maintaining or improving privacy protection quality.
Solution Approach 2:
The system achieves full automation by performing all anonymization tasks independently. The deep learning model self-identifies features requiring anonymization, selects appropriate obscuration techniques based on feature type, and executes the obscuration without any human input, maximizing the extent of automation.
3Reliability
If consistent anonymization across multiple images is required, then privacy protection is improved, but processing time increases
Solution Approach 1:
The system performs preliminary classification of identifying features using deep learning models before applying obscuration. By pre-training and pre-processing feature detection and classification, the system establishes consistent anonymization rules across multiple images, ensuring uniform privacy protection while maintaining efficient processing through automated batch operations.
Data Source
AI summary
A computer-implemented method of anonymizing at least one feature in a scene comprises receiving one or more images representing the scene and applying a sorting algorithm to the image(s) to identify features within the image(s) and classify the features according to at least one predetermined feature category to produce at least one classified feature in the image(s). An annotation scheme, specifying at least one obscuring annotation type to be applied to at least one corresponding feature category, is applied to the image(s). The image(s) are then modified by, for each classified feature in the image(s) having an annotation indicating that said classified feature is in a feature category corresponding to a particular obscuring annotation type to be applied, applying the particular obscuring annotation type to that classified feature in the image(s) so that at least one identifying aspect of that classified feature is obscured within the image(s).