Technology

How Sentinel Core Vision works

A real-time vision pipeline that runs entirely on your machine — every stage visible, swappable, and tunable.

The pipeline

Six stages, one continuous loop

Every frame flows through the same path — from capture to output — and each stage is exposed for inspection and tuning.

1 · Capture

Frames come in from a screen, a camera, or a capture card through the operating system's media stack — at the source's native resolution and refresh.

Screen · camera · capture card

2 · Preprocess

Each frame is resized and normalized into the shape the model expects — fast, GPU-assisted frame prep that keeps the loop tight.

GPU-assisted frame prep

3 · Inference

An ONNX Runtime model scans the prepared frame and returns detections — the objects it sees and where they are.

ONNX Runtime models

4 · Tracking

Detections are stitched into persistent identities across frames. Choose from 11 swappable tracking engines with predictive motion.

11 swappable engines

5 · Color fusion

An optional HSV color check runs alongside the model, confirming the object by its color before anything acts on it.

Optional HSV confirmation

6 · Output

The result is delivered with low latency to a supported input device, so it reads like native input.

Low-latency delivery
On-device

Runs on your hardware

The whole pipeline lives on your PC — and it's built to make the most of the GPU you already have.

Local-first

100% on-device processing

Every frame is captured, prepared, analyzed, and acted on right on your machine. Nothing about what you're looking at is uploaded — frames never leave your PC. The only network call is a lightweight license check to confirm your subscription.

  • Frames are processed locally, never uploaded
  • No cloud dependency in the vision loop
  • Only network traffic is the license check
GPU stack

A multi-provider GPU stack

Sentinel runs on ONNX Runtime and picks the right acceleration path for your machine: NVIDIA cards via CUDA and TensorRT, AMD and Intel via DirectML. When a provider isn't available on your system, it falls back automatically so the engine keeps running.

  • NVIDIA via CUDA / TensorRT
  • AMD & Intel via DirectML
  • Automatic fallback when a provider is missing
Models

Hot-swappable ONNX models

Load a different ONNX model on the fly — no restart, no downtime. Swap detection models in seconds to compare accuracy and speed, and keep the one that fits your setup best.

  • Drop-in ONNX model loading
  • No restart to change models
  • Compare models side by side quickly
Dashboard

A local dashboard at localhost:5000

The whole engine is driven from a dashboard in your browser at http://localhost:5000. Watch live system health, inspect every decision on the vision view, and tune each stage in real time — no config files, no command line.

  • Live tuning and inspection in the browser
  • System health and pipeline state at a glance
  • Every parameter is a labeled control
Performance

Built for low latency

The pipeline is engineered for high-frequency, low-latency operation — from frame in to output out.

The stack it runs on
ONNX Runtime NVIDIA CUDA TensorRT DirectML AMD Intel HSV color fusion 11 tracking engines

Real-world latency and frame rate depend on your hardware — your GPU, drivers, the model you run, your capture resolution, and your configuration all affect the numbers you see.

See the whole pipeline for yourself

Activate in one click and watch every stage run live in your browser — capture to output, all on your machine.