The retail industry faces increasing pressure to deliver engaging, personalized in-store experiences while respecting customer privacy. Digital signage networks and audience measurement systems would benefit from isolating human subjects from complex store backgrounds in real time, enabling dynamic content adaptation, anonymous footfall analytics, and immersive visual displays without capturing identifiable environmental data.
Traditional chroma-key or static background solutions fall short under the dynamic, uncontrolled lighting conditions typical of retail spaces. An AI-driven background removal solution, optimized for edge deployment, would address these challenges by providing accurate, real-time person segmentation directly within existing GStreamer-based multimedia pipelines, eliminating cloud dependency and minimizing latency.
Conceptual Design
AI Pipelines in Retail Signage
How AI-powered background removal would transform retail digital signage
A GStreamer-based pipeline would capture video feeds from in-store cameras and process them through the Fluendo AI Plugins background removal element. Leveraging deep learning models optimized for real-time inference via the Raven AI Engine, the system would segment people from their surroundings with sub-frame latency.
The processed video—with backgrounds removed or replaced—could then be routed to digital signage displays for interactive content, or to audience measurement modules that analyze anonymized silhouettes for footfall counting, dwell-time estimation, and demographic profiling without retaining personally identifiable information.
By running entirely on edge devices with GPU acceleration (NVIDIA, AMD, or Intel), the solution would eliminate cloud dependency, ensuring data never leaves the premises. The modular GStreamer architecture would allow retailers to integrate background removal into existing video infrastructure with minimal reconfiguration.
AI Pipelines in Retail Signage
How AI-powered background removal would transform retail digital signage
A GStreamer-based pipeline would capture video feeds from in-store cameras and process them through the Fluendo AI Plugins background removal element. Leveraging deep learning models optimized for real-time inference via the Raven AI Engine, the system would segment people from their surroundings with sub-frame latency.
The processed video—with backgrounds removed or replaced—could then be routed to digital signage displays for interactive content, or to audience measurement modules that analyze anonymized silhouettes for footfall counting, dwell-time estimation, and demographic profiling without retaining personally identifiable information.
By running entirely on edge devices with GPU acceleration (NVIDIA, AMD, or Intel), the solution would eliminate cloud dependency, ensuring data never leaves the premises. The modular GStreamer architecture would allow retailers to integrate background removal into existing video infrastructure with minimal reconfiguration.
The value we deliver: How we boost your business
Real-Time Performance on Edge Devices
Optimized for GPU-accelerated inference on NVIDIA, AMD, and Intel hardware, the solution would deliver real-time background segmentation at Full HD resolution on compact edge devices, ideal for the space and power constraints of retail installations.
Integration and flexibility
Easy to set up an immersive and branded scene, completely integrated in your business ecosystem.
Scalable Across Retail Locations
The modular, software-only architecture would allow deployment across hundreds of retail sites with centralized configuration management, adapting to different store layouts and camera setups without per-location customization.
Real-Time Performance on Edge Devices
Optimized for GPU-accelerated inference on NVIDIA, AMD, and Intel hardware, the solution would deliver real-time background segmentation at Full HD resolution on compact edge devices, ideal for the space and power constraints of retail installations.
Integration and flexibility
Easy to set up an immersive and branded scene, completely integrated in your business ecosystem.
Scalable Across Retail Locations
The modular, software-only architecture would allow deployment across hundreds of retail sites with centralized configuration management, adapting to different store layouts and camera setups without per-location customization.