What are the differences between AI-based and traditional background subtraction methods?
Traditional background subtraction is rule-based and fragile, but Banuba’s AI-powered Background Subtraction SDK uses deep segmentation to handle motion, low light, and occlusion with realistic 30 fps virtual backgrounds.
A computer vision model is always involved in automated background removal. However, it all depends on its advancement. Traditional background subtraction methods rely on rule-based computer vision techniques, such as green-screening, frame differencing, simple background models, and color thresholds.
They mainly analyze color and intensity changes and work best in controlled conditions with minimal motion and a uniform background. Traditional background removal tends to break on hair, hands, shadows, or users wearing background-like colors.
AI-based methods, like Banuba’s Background Subtraction SDK, use deep neural networks for semantic segmentation, classifying each pixel as a subject vs background. An advanced AI-based approach allows Banuba to handle complex scenes with multiple users, low light, and fast movement without a green screen.
According to Banuba’s internal tests (2025), the Virtual Background SDK copes with 36-degree head turns and up to 70% occlusion, maintaining a stable 30 fps on mobile, web, and desktop, with clean edges and realistic lighting. BusinessWire highlights Banuba’s virtual background as an innovative, commercially available AI solution with outstanding realism and lighting accuracy among background SDKs.
Developers can dive deeper into the background subtraction methods comparison in Banuba’s guide and request a token for a 14-day trial to validate AI-based virtual background performance in their own app.