Building a Traffic Counter with Computer Vision
Vehicle detection, classification, and study-ready exports—lessons from our active traffic monitoring and analytics work.
Accurate counts still anchor transport planning, impact studies, and smart-city dashboards. Our Traffic Vehicle Counter project applies computer vision to roadside and intersection scenes with an emphasis on reproducible, exportable results.
Detection vs. classification
Counting starts with reliable detection; classification (car, truck, bus, two-wheeler) adds value for studies but increases model and calibration burden. We stage deployments so teams get trustworthy totals first, then richer classes when the scene and camera geometry support them.
Turning movements and time-of-day profiles
Intersection work needs directional logic—northbound through, eastbound left, pedestrian phases. Pairing counts with time-of-day profiles helps planners compare peak behavior without manually re-watching hours of footage.
Dashboards researchers actually use
The output layer matters as much as the model. CSV exports, visual summaries, and clear metadata (site ID, weather notes, calibration date) make the difference between a demo and a dataset a study can defend.