Real-time in-cabin monitoring on the edge

Problem and challenges

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.

Solution

We developed real-time monitoring using rear-view-mirror cameras for 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

Outcome

• 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.