Deterministic Object Alignment for Low-Cost Background Removal
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Solution Overview
Problem
Current background removal and foreground retrieval methods in video conferencing are either computationally expensive or require manual, repetitive user intervention, leading to performance penalties and user frustration.
Innovation Solution
Automated capture of user-free background images using deterministic object alignment, which involves capturing a background image without users or foreground objects and aligning it with subsequent images using a deterministic object as a reference, allowing for efficient background removal and foreground retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated pixel-based brute force approach is used for background removal, then background removal can be performed automatically, but computational cost becomes incredibly expensive
Solution Approach 1:
The system captures a background image beforehand when the foreground object is absent, storing it for later use. This preliminary capture eliminates the need for continuous computational comparison during video conferencing, as the pre-captured background image serves as a reference for subtracting foreground elements from subsequent frames.
Solution Approach 2:
The system creates a copy of the background scene captured when no foreground object is present. This background copy is then used as a reference image to subtract from video frames containing the foreground object, enabling efficient background removal without requiring complex real-time computational algorithms.
2Ease of operation
If manual calibration approach is used for background removal, then user control over background image is achieved, but user intervention is required repeatedly when conditions change
Solution Approach 1:
The system automatically detects when the foreground object has left the frame and autonomously captures a new background image without requiring user intervention. This self-service mechanism allows the system to adapt to changing conditions automatically, eliminating the need for users to manually recalibrate when conditions change.
Solution Approach 2:
The system continuously monitors the video feed to detect when the foreground object has exited the frame. This feedback mechanism triggers automatic recapture of the background image, ensuring the background reference remains up-to-date without requiring user awareness or action.
3Reliability
If manual calibration is performed in advance, then background image can be captured, but calibration must be repeated every time conditions change
Solution Approach 1:
The system captures the background image in advance when the foreground object is absent, preparing the background reference beforehand. This preliminary action ensures accurate background capture while allowing the system to automatically update when conditions change, maintaining both reliability and productivity.
Solution Approach 2:
The system transitions from static manual calibration to dynamic automatic updates. The background image is initially captured manually or automatically, then automatically updated whenever the foreground object leaves the frame, making the calibration process adaptive to changing conditions without requiring repeated user intervention.
Data Source
AI summary
In one embodiment, a device may obtain a user-free first image of a background portion of a physical environment including a deterministic object. The device may then locate the deterministic object in a second image of the physical environment. The device may then generate, based on the deterministic object, an alignment between the user-free first image and the second image. The device may then use the alignment for background removal in subsequent images of the physical environment.