The Camera Scaling Problem
Today
Rail operators manage thousands of grade crossings. Ports deploy hundreds of cameras across container yards. Every camera adds bandwidth pressure on hub-and-spoke networks.
AI analysis is often done at the edge or not at all — streaming all feeds to a central location is prohibitively expensive.
MAHAMAIA
Compress neural features at each camera node. Transmit only what AI models need to a central operations center or cloud.
Scale camera coverage across rail corridors and port facilities without upgrading network infrastructure. One stream serves unlimited downstream models.
Day in the Life
Grade crossing camera triggers
Train approach detected. Edge encoder begins feature extraction from the crossing feed.
MAHAMAIA compresses features
The learned compressor reduces each frame to a compact latent on the local processor.
Edge AI processes locally
Obstruction detection runs immediately at the crossing. Alert generated in milliseconds.
Central operations analyzes
Compressed feature stream arrives at operations center. Fleet tracking and crossing analytics run simultaneously.
Operator receives status
Real-time visibility into every crossing and yard camera. No dedicated video backhaul required.
More cameras. Same network.
Pilot program available for rail and logistics operators. See how MAHAMAIA fits your infrastructure.
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