Surveillance Anonymization via Animated Person Models
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Solution Overview
Problem
Existing surveillance image anonymization methods, such as pixelation, render data unusable for training and testing of image processing systems, as they make individuals unrecognizable, violating data protection regulations and hindering the development of algorithms like those for autonomous driving.
Innovation Solution
An anonymization apparatus comprising a recognition module to detect persons, an estimation module to estimate movement features, and a processing module to replace recognizable persons with animated person models, ensuring that anonymized images retain behavior and interactions while conforming to data protection regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pixelation or black boxes are used to anonymize persons in surveillance images, then personal data protection is improved, but the usability of data for training and testing image processing systems deteriorates
Solution Approach 1:
The patent creates synthetic person models that copy the movement patterns and behaviors of real persons while using artificially generated appearances. These synthetic models are inserted into surveillance images to replace real persons, thereby protecting personal data while preserving the movement information needed for training image processing algorithms.
Solution Approach 2:
The patent introduces synthetic person models as an intermediary between real persons and the training data requirements. These synthetic models act as mediators that convey movement patterns and behavioral information without exposing identifiable personal data, thus bridging the gap between data protection and algorithm training needs.
2Measurement precision
If real surveillance images are used for training image processing systems, then training data quality is improved, but personal data protection compliance deteriorates
Solution Approach 1:
The patent generates synthetic person models that replicate the movement patterns, gestures, and behaviors of real persons captured in surveillance images. These synthetic copies preserve the essential training characteristics while using artificially generated appearances that cannot be traced back to real individuals, thus maintaining training data quality while ensuring data protection compliance.
3Reliability
If persons are made completely unrecognizable through anonymization, then personal data protection is improved, but the retention of behavior and interaction information deteriorates
Solution Approach 1:
The patent separates the identification information (appearance, identity) from the behavioral information (movement patterns, gestures, interactions) of persons in surveillance images. By extracting only the behavioral characteristics and applying them to synthetic person models with generated appearances, the system protects personal identity while preserving behavior and interaction data for training purposes.
Data Source
AI summary
An anonymization apparatus 6 is proposed for the generation of anonymized images 9, wherein surveillance images 5 are provided through video surveillance of a surveillance region 3 by means of at least one camera 2, with a recognition module 11, wherein the surveillance images 5 are provided to the recognition module 11, wherein the recognition module 11 is configured to recognize persons 4 contained in the surveillance images 5, with a processing module 13, wherein the processing module 13 is configured to process the surveillance images 5 into the anonymized images 9, wherein at least one person 4 or person segment included in the surveillance images 5 is anonymized in the anonymized images 9, wherein the processing module 13 is configured to replace the recognized person 4 or person segment by an animated person model 14 for the purpose of anonymization.