Video Feed Segmentation for Distraction Correction
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Solution Overview
Problem
Current methods for reducing undesirable content during video communications, such as distractions or unintended gestures, are inefficient and require manual intervention, disrupting the communication process.
Innovation Solution
A system and method for detecting and correcting distracting content by segmenting video feeds to distinguish background and foreground data, classifying qualifying behaviors as distracting, and applying effects, such as blurring, to reduce their appearance when a probability score exceeds a threshold, thereby minimizing disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If manual intervention is used to turn off video feed or apply background effects, then undesirable content is reduced, but communication continuity is disrupted
Solution Approach 1:
The system performs preliminary detection and classification of background content before it becomes a distraction. By continuously monitoring and pre-processing video feeds, the system identifies potential distractions and applies corrections automatically, preventing communication disruption while maintaining video quality
Solution Approach 2:
The system enables self-service by automatically detecting, classifying, and correcting distracting content without requiring manual user intervention. The automated correction mechanism applies effects like blurring or masking to distracting elements, allowing communication to continue uninterrupted while maintaining professional video quality
2Productivity
If automated detection and correction is implemented, then communication continuity is maintained, but system complexity increases
Solution Approach 1:
The system segments the video processing task into distinct modules: video feed reception, background/foreground segmentation, distraction detection, classification, and correction application. This modular architecture manages complexity by dividing the automated system into manageable, independent components that can be processed sequentially
Solution Approach 2:
The system introduces an intermediary classification component that acts as a mediator between detection and correction. This intermediary layer analyzes detected distractions, determines their type and severity, and selects appropriate correction effects, thereby managing system complexity through layered processing
3Object-affected harmful factors
If background effects are applied manually, then distracting content is reduced, but user convenience is compromised
Solution Approach 1:
The system enables self-service by automatically detecting, classifying, and correcting distracting content without requiring manual user intervention. The automated correction mechanism applies effects like blurring or masking to distracting elements, allowing communication to continue uninterrupted while maintaining professional video quality
Solution Approach 2:
The system implements feedback by continuously monitoring video feeds and automatically adjusting corrections based on detected distractions. The real-time feedback loop ensures that distracting content is promptly addressed while maintaining natural video appearance, enhancing user convenience through adaptive automated management
Data Source
AI summary
Aspects of the present disclosure relate to systems and methods for detecting and correcting undesirable content. A video feed may be segmented to distinguish background data from foreground data. It may be determined that a region of the background data includes a qualifying behavior. The qualifying behavior may be classified as belonging to a distracting category of data. An effect may be applied to the background data that includes the qualifying behavior to reduce an appearance of the qualifying behavior.