Video Privacy System Using Machine Learning Context Detection
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Solution Overview
Problem
In augmented reality and livestreaming environments, ensuring real-time privacy protection for sensitive content is challenging due to the overwhelming amount of data generated, which traditional methods are ill-equipped to handle effectively.
Innovation Solution
A method and system utilizing machine learning to continuously monitor video data, determine privacy contexts, and apply appropriate obfuscation techniques such as blocking or filtering sensitive portions, leveraging user-defined tags, crowdsourced data, and machine learning algorithms to ensure dynamic privacy protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to handle video data, then device complexity is reduced, but privacy protection effectiveness deteriorates due to overwhelming data volume
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the video camera and the privacy protection system. The model pre-processes video data by identifying and flagging sensitive content (faces, license plates, etc.) before the main privacy protection logic processes it. This intermediary layer enables effective privacy protection without requiring the entire system to handle all video data at full complexity.
Solution Approach 2:
The privacy protection system is segmented into multiple specialized modules: a machine learning model for content identification, a context determination module for understanding scene meaning, and a blocking module for applying privacy protections. Each module handles a specific aspect of the problem, making the overall system more manageable and effective despite the complexity.
2Measurement precision
If real-time monitoring of all video data is performed, then privacy protection accuracy is improved, but processing speed deteriorates due to data volume
Solution Approach 1:
The machine learning model performs preliminary identification of potentially sensitive content before the full privacy protection processing occurs. By pre-flagging regions that may contain personal information, the system avoids processing every pixel and frame in detail, maintaining both accuracy and speed.
Solution Approach 2:
Instead of applying full privacy protection processing to all video data, the system applies intensive processing only to regions identified by the machine learning model as potentially containing sensitive information. This partial action approach maintains precision where needed while preserving overall processing speed.
3Measurement precision
If machine learning algorithms are deployed for content identification, then privacy protection accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The computationally intensive machine learning content identification function is extracted as a separate, specialized module that operates independently from the main video processing pipeline. This extracted component can be optimized specifically for low-power operation or deployed on dedicated hardware accelerators, reducing the overall energy burden on the main system.
4Reliability
If dynamic blocking of video data is applied, then privacy protection effectiveness is improved, but loss of useful video information increases
Solution Approach 1:
Instead of applying uniform blocking across entire video frames, the system applies privacy protections only to specific local regions identified as containing sensitive information (such as faces or license plates). The rest of the video data remains unobstructed, maintaining both privacy protection effectiveness and video information quality.
Data Source
AI summary
A method, system and computer program product for providing video privacy is provided herein. First video data captured by a video camera is received. A context for the first video data is determined. It is determined that the context matches a privacy context from a set of privacy contexts identified using machine learning. In response to the context matching the privacy context, at least a portion of second video data is blocked that is captured by the video camera subsequent to the first video data.