Real-time in-cabin monitoring on the edge
Computer vision for in-cabin monitoring — pose, emotion, and object detection from rear-view-mirror cameras, optimized to run in constrained embedded environments.
Challenge
In-cabin monitoring has to run in real time on hardware with tight compute, memory, and power budgets. Cloud round-trips aren’t an option, and accuracy can’t collapse under real-world lighting and motion. Perception models built for the data center don’t survive on the edge without serious engineering.
Approach
We developed real-time monitoring using rear-view-mirror cameras — pose, emotion, and object detection — optimized for constrained embedded environments. The work balanced model accuracy against strict latency and resource budgets, so perception runs locally and reliably inside the vehicle.
System design
- Perception models for pose, emotion, and object detection
- Optimization for constrained embedded compute and memory
- Real-time, on-device inference without cloud round-trips
- Robustness to real-world lighting and motion
What we delivered
- A real-time in-cabin monitoring system on the edge
- Multi-task perception within embedded resource budgets
- Reliable on-device inference under real conditions
- A perception foundation suited to automotive constraints
Why it mattered
Edge perception is an engineering discipline as much as a modeling one. By designing for the hardware from the start, the system delivers real-time monitoring where it has to live — inside the vehicle, on constrained hardware, in real conditions.
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