version: "1.0.0" name: cvedia-setup description: Install and run CVEDIA-RT AI inference engine on NVIDIA Jetson for pre-built computer vision applications (crowd estimation, drone detection, vehicle counting, etc.). Requires JetPack 5.1 and a CVEDIA account.

CVEDIA-RT on NVIDIA Jetson

CVEDIA-RT is a modular, cross-platform AI inference engine with pre-loaded applications: crowd estimation, drone detection, fall detection, lane occupancy, vehicle type counter, package detection, and more.

Hardware: NVIDIA Jetson device (tested with reComputer J4012) Software: JetPack 5.1 with all SDK components, internet connection Account: CVEDIA (free sign-up at rt.cvedia.com)

Execution model

Run one phase at a time. After each phase:

• If output contains [STOP] → stop immediately, consult the failure decision tree
• If output ends with [OK] → tell the user "Phase N complete" and proceed

Phase 1 — prerequisites check (~30 s)

cat /etc/nv_tegra_release
# Expected: R35 (release) for JetPack 5.x

dpkg -l | grep nvidia-jetpack

[OK] when JetPack 5.1+ with SDK components confirmed. [STOP] if JetPack version is incompatible.

Phase 2 — download CVEDIA-RT (human action + ~5 min)

1. Visit https://rt.cvedia.com/ and sign in (or create account)
2. Click Download under "NVIDIA Jetson"
3. Select "Docker (Recommended)" to download the tar.gz installer

Transfer the file to the Jetson device.

[OK] when tar.gz file is on the Jetson.

Phase 3 — install CVEDIA-RT (~3 min)

mkdir -p ~/cvedia && cd ~/cvedia
tar -xzvf <filename.tar.gz>
sudo ./install.sh

Respond to installer prompts as needed.

[OK] when installation completes without errors.

Phase 4 — run CVEDIA-RT (~1 min)

With internet (first run, downloads models):

./run.sh

Without internet (after first online run):

./run.sh -U

Expected: CVEDIA-RT application opens with pre-loaded solutions.

[OK] when the application UI is visible.

Phase 5 — explore applications (~5 min)

1. Click on a solution category (e.g. intelligent-transportation-systems)
2. Click the run button next to a solution (e.g. lane-occupancy)
3. Wait for model and video files to download
4. Configure video source: gear icon → Edit Source → select camera/file → Save Instance
5. Stop and restart the application for changes to take effect

[OK] when inference results are visible on the video feed.

Failure decision tree

Reference files

• references/source.body.md — Original Seeed wiki with screenshots and application examples