Video Streaming Resource Management via User Attention Detection
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Solution Overview
Problem
Video streaming services face inefficiencies in managing resource usage on user devices when users are not paying attention to the presentation, leading to unnecessary resource consumption and potential battery drain or increased network load.
Innovation Solution
Implementing a system that detects user attention by analyzing real-time images for predefined facial features, such as eyes, to determine if the user is attentive, and subsequently reduces resource consumption by adjusting video quality, brightness, or discontinuing the video playback if the user is not paying attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If video streaming continues at high quality level, then user experience is maintained, but device resources and battery are wasted when user is not paying attention
Solution Approach 1:
The system continuously monitors user attention state through facial feature detection and uses this feedback to dynamically adjust video streaming quality. When the user is detected as inattentive, the system reduces video quality or pauses streaming, and restores quality when attention is detected, creating a closed-loop control system that adapts to user state.
Solution Approach 2:
The video streaming quality is made dynamic rather than static. The system adjusts video parameters (quality level, pause/resume) based on real-time user attention detection, allowing the streaming behavior to change adaptively according to user engagement state throughout the viewing session.
2Loss of energy
If video streaming is continuously delivered, then content availability is ensured, but network resources are wasted when user is not paying attention
Solution Approach 1:
Instead of continuously delivering full-quality video regardless of user state, the system applies partial action by reducing video quality or pausing streaming when the user is inattentive. This provides just enough content delivery to maintain session readiness while avoiding excessive network resource consumption during periods of user disengagement.
3Measurement precision
If real-time facial detection is implemented, then user attention can be accurately detected, but device processing load increases
Solution Approach 1:
The system extracts only the essential facial features (eyes, eyebrows, mouth) needed for attention detection rather than processing the entire facial image or performing complex analysis. This selective extraction of critical features reduces processing load while maintaining sufficient accuracy for determining user engagement state.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a method including detecting attention by a user to a presentation displayed at an audiovisual presentation device. A real-time image of the user is obtained and stored local to the audiovisual presentation device. The image is analyzed to determine a presence in the image of a facial feature of the user; the device determines, based on the presence of the facial feature, whether the user is attentive to the presentation. When the user is not attentive to the presentation, a procedure is initiated at the audiovisual presentation device to reduce resource consumption by the device; the procedure can include continuing the presentation at a video quality level lower than at the beginning of the presentation, reducing a brightness of the display, continuing the presentation with only the audio portion, and discontinuing the presentation. Other embodiments are disclosed.