Video Stream Privacy Adjustment via Attention Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional video conferencing systems face challenges in managing participant availability and privacy, particularly with always-on video streams that can compromise privacy when participants are not paying attention.
Innovation Solution
A system that determines user attention levels using RGB and depth image capture devices, modifies video stream data by blurring and muting participants who are not paying attention, and transmits these modifications to other participants in real-time, ensuring privacy during inattention periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an always-on video stream is used to address participant availability, then participant availability is improved, but privacy is worsened
Solution Approach 1:
The video stream transitions from a static always-on state to a dynamic state that automatically adjusts based on detected user attention. The system continuously monitors attention levels and modifies the video stream accordingly, making it visible when the user is attentive and blurred when inattentive, thus resolving the contradiction between availability and privacy.
Solution Approach 2:
The system changes the visual parameter of the video stream (clarity vs. blur) based on the user's attention state. When the user is detected as inattentive, the video stream parameter is modified to be blurred, automatically reducing privacy exposure without requiring manual intervention or disrupting the always-on availability.
2Object-affected harmful factors
If manual privacy adjustments are implemented, then privacy control is improved, but ease of operation is worsened
Solution Approach 1:
The system performs automatic privacy management by monitoring user attention and adjusting the video stream without requiring manual user actions. The privacy control mechanism serves itself by detecting when the user is inattentive and automatically blurring the video stream, eliminating the need for users to manually adjust privacy settings.
Solution Approach 2:
The system implements a feedback loop where the user's attention state is continuously monitored and fed back to the video stream processing system. This feedback mechanism enables automatic adjustment of privacy levels based on real-time user state, removing the need for manual intervention while maintaining appropriate privacy control.
Data Source
AI summary
Methods and systems may involve determining a user attention level based on video stream data associated with a first participant in an always-on video conferencing session. The video stream data may be modified based on the user attention level to obtain modified video stream data that is automatically adjusted for privacy. In addition, the modified video stream data may be transmitted to one or more other participants in the video conferencing session.