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Artificial Intelligence · Major Project
Vehicle Counting, Classification & Speed Detection
Counts and classifies traffic and estimates speed from video
3-4 weeks build
Overview
Road footage is processed with YOLOv8 to classify cars, bikes, buses, trucks and autorickshaws, and each vehicle is tracked across frames. A homography calibrated from known road markings maps pixels to metres, so speed comes from real distance over time. Counts per class and lane, plus overspeed events, are exported for a traffic study.
What makes it stand up
- 01Vehicle classes including two-wheelers and autorickshaws
- 02Tracking so each vehicle is counted exactly once per lane
- 03Homography calibration from real road markings
- 04Speed estimate with overspeed flagging and snapshots
- 05Speed error checked against a known-speed reference clip
Module breakdown
- 01
Detection
YOLOv8 with classes suited to Indian roads.
- 02
Tracking
Track IDs, lane assignment and line counting.
- 03
Calibration
Four-point homography from pixels to ground plane.
- 04
Speed
Smoothed displacement over time in km/h.
- 05
Reports
Streamlit dashboard and CSV of counts and speeds.
After this, you will be able to
- Explain how homography turns pixels into metres
- Defend the sources of error in your speed estimates
- Demo live counts and speeds on a highway clip
Technology stack
- Python
- YOLOv8
- ByteTrack
- OpenCV
- Streamlit
- Pandas
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support