DUI-Vision

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  Local Layout that displays suspect test data in real time.

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  Centered pupils detected within the green boxes.

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  Centered pupils detected within the green boxes.

Inspiration

I've attended police academies at both my last university, Duke, and at Stanford. Last Wednesday in class, I watched a live Standard Field Sobriety Test (SFST) training (and conducted tests of my own on drunk volunteers). Learning about the process was frustrating -- a single DUI takes at least 4-6 hours of paperwork, keeping officers off the streets from saving lives. The DUI-Vision automates the grunt work of sobriety testing and provides complementary evidence in court.

What it does

Hardware-integrated Edge AI assistant using body-cam feed to perform real-time Horizontal Gaze Nystagmus (HGN) detection on Jetson Nano. -- HGN is the sobriety test with the highest accuracy rate; it looks at movement of the eye when following an object or at certain angles; inebriated persons will have uncontrollable movement of the eye -- Model detects eyes and pupils automatically and tracks involuntary eye patterns (spasms) -- Automatically uses data to start populating the NHTSA-standard HGN reports (e.g. scores/eye) -- All processing is done entirely on the device

How we built it

Hardware: NVIDIA Jetson Orin Nano Super + USB Camera Software: Custom pupil-tracking algorithm using OpenCV and NanoOwl Jitter Engine: Filters head movement from actual Nystagmus jerking and differentiates between frequential spasms of eye vs. normal smooth movement Edge Stack: Local FastAPI server and CSS dashboard on Jetson to serve real-time results

Challenges we ran into

This hackathon was an absolute hardware war for me. As an EE it was humbling to see how much work there is going to just make a connection on "software." I spent hours figuring out SSH/Serial Console and combatting network collisions. I've never used a terminal or SSH'ed into a machine before this, or even used codex to help edit code so it was a steep learning curve.

Accomplishments that I'm proud of

First hackathon & Competing Solo Not quitting dealing with the difficult hardware and unstable network Not throwing the jetson on the ground after another connection failure Having a working product and local web server running real time

What I learned

Developer kits are a lot harder than they look. Latency is so important for edge AI products. AI Code tools can help a lot but if you don't have a clear architectural vision or idea it can't do much. Once the project grows big the errors become more and more. Kind of like a cancer.

What's next for DUI-Vision: Field Sobriety Test (FST) Edge Assistant

Multi-Modal Agent -- integrating Whisper on a second Jetson to transcribe the suspect interview and auto-fill initial contact and arrest forms. Rest of the SFST -- adding CV models for the "Walk and Turn" and "One Leg Stand" tests, among many. Miniaturization -- making the logic work on a Orin-based body cam rig or recording glasses

Built With

• css
• csv
• html
• javascript
• jetson-nano
• nanoowl
• numpy
• opencv
• pillow
• python
• pytorch
• rest
• sqlite