TrafficStream2026
Lex Traffic + YOLO
Overview
Real-time vehicle detection on the City of Lexington, KY public traffic cameras using the latest Ultralytics YOLO model. It identifies and counts bicycles, cars, motorcycles, and buses.
TrafficStream pulls the live camera map from Lexington's public traffic site and runs YOLO object detection in one of two modes:
(On macOS/Linux use source .venv/bin/activate.)
Press q (or close the window) to quit.
The live viewer is built to play at real time and never freeze the machine. The City's live HLS feeds are delivered in bursts — OpenCV/FFmpeg decodes a couple of seconds of video almost instantly, then stalls until the next segment — so a naive "show the newest decoded frame" viewer fast-forwards through each burst and then freezes. Instead, the viewer:
A live FPS / inference-latency overlay shows headroom, and the resolved GPU name is printed at startup so you can confirm inference is on your GPU.
From the project’s README on GitHub.
At a glance
| Repository | Kentucky-Open-Science/TrafficStream2026 |
|---|---|
| Research area | Computer vision |
| Primary language | Python |
| License | Apache-2.0 |
| Stars / forks | 0 / 0 |
| Open issues and pull requests | 0 |
| Created | 2026-06-18 |
| Last push | 2026-06-18 |
| Default branch | main |
| Topics | computer-vision, object-detection |
What the README covers
- How it works
- Requirements
- Setup
- Usage
- Live: stream one camera
- Batch: vehicle density across all cameras
- Batch: classify saved images with the largest model
- Vehicle classes
- Project layout
- Model
- Data source
- License
Top contributors
- @armstrongsam25 (3 commits)
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Get the code
git clone https://github.com/Kentucky-Open-Science/TrafficStream2026.git