Processing 4 Cameras at 25 FPS with 15W Power

4 Cameras. 25 FPS. 15 Watts.

Traditional ALPR deployments require one processing unit per camera lane. VEBZE's architecture inverts this: a single 15W edge device handles four simultaneous streams at full 25 FPS — with plate recognition running on every frame.

How It Works

The key is model compression and pipeline parallelism:

1. Shared inference engine — one TensorRT model instance processes batches from all four cameras
2. Frame multiplexing — incoming frames are interleaved and dispatched as a single batch
3. Async result routing — results are routed back to the originating stream without blocking

Power vs Performance

Configuration	Power Draw	Throughput
1 camera, 25 FPS	5W	25 FPS
2 cameras, 25 FPS	9W	50 FPS total
4 cameras, 25 FPS	14.8W	100 FPS total
4 cameras, 30 FPS	17.2W	120 FPS total

Infrastructure Impact

A 4-lane parking entrance that previously needed 4× GPU-equipped servers (~400W combined) now runs on a single Jetson edge device at 15W.

Cost reduction: ~94% in hardware, ~80% in power.

Use Cases

• Parking garages: 4 entry/exit lanes on one device
• Toll plazas: multi-lane simultaneous capture
• Border crossings: high-throughput, air-gapped operation
• Industrial yards: low-power always-on monitoring

Conclusion

15W for 4 cameras at 25 FPS is not a compromise — it is the architecture. VEBZE's pipeline eliminates the 1:1 camera-to-server assumption and makes dense multi-lane ALPR economically viable at any scale.