cvedia-setup

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

Symptom	Action
install.sh fails	Verify Docker is installed with nvidia runtime. Check JetPack version.
run.sh hangs or crashes	Check Docker logs. Ensure sufficient disk space and memory.
Application won't start offline	Must run each solution at least once with internet first.
Video source not working	Stop application, reconfigure source, restart. Check camera connectivity.
Low FPS / poor performance	Ensure JetPack SDK components are installed. Check GPU utilization with tegrastats.

---

Reference files

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