YOLOv8 for Traffic Monitoring: GPU Guide

Why YOLOv8 for Traffic Monitoring

Urban traffic management, highway monitoring and smart city initiatives all require accurate, real-time vehicle detection and classification. YOLOv8 detects and tracks cars, lorries, buses, motorcycles, bicycles and pedestrians simultaneously, enabling automatic traffic counting, speed estimation, congestion detection and incident alerting from existing roadside cameras.

With object tracking, YOLOv8 follows individual vehicles across frames to calculate journey times, identify wrong-way driving and detect stopped vehicles. This data feeds directly into traffic management systems, adaptive signal control and real-time traveller information services.

Running YOLOv8 on dedicated GPU servers provides the processing power for multi-junction deployments. A vision model hosting deployment ensures consistent detection in all weather and lighting conditions, 24 hours a day.

GPU Requirements for YOLOv8 Traffic Monitoring

The number of monitored junctions determines GPU requirements. Below are tested configurations. For detailed FPS data, see our YOLOv8 FPS by GPU benchmarks.

Check current availability on the vision model hosting page, or browse all options in our dedicated GPU hosting catalogue.

Quick Setup: Deploy YOLOv8 for Traffic Monitoring

Spin up a GigaGPU server, SSH in, and run the following to begin processing traffic camera feeds. For GPU selection guidance, see our best GPU for YOLOv8 guide.

# Deploy YOLOv8 for traffic monitoring with vehicle tracking
pip install ultralytics opencv-python-headless
python -c "
from ultralytics import YOLO
model = YOLO('yolov8m.pt')
# Track vehicles with class filtering
results = model.track(
    source='rtsp://traffic_cam:554/stream',
    tracker='bytetrack.yaml',
    stream=True, conf=0.4,
    classes=[2, 3, 5, 7],  # car, motorcycle, bus, truck
    save=False, show=False
)
for r in results:
    vehicles = len([b for b in r.boxes if b.id is not None])
    print(f'Active tracked vehicles: {vehicles}')
"

This provides the detection backbone for traffic analytics. For general surveillance approaches, see YOLOv8 for Video Surveillance.

Performance Expectations

YOLOv8m processes 1080p traffic camera feeds at approximately 80 FPS on an RTX 5090 with tracking enabled. Vehicle classification accuracy exceeds 92% across standard vehicle categories in daylight conditions, with night performance depending on camera IR capability.

Actual results vary with camera quality and weather conditions. Our FPS benchmark data and performance benchmarks provide detailed comparisons. For aerial detection, see YOLOv8 for Drone/Aerial Detection.

Cost Analysis

Commercial traffic monitoring systems cost £10,000-£50,000 per junction including hardware, software licensing and maintenance. YOLOv8 on a dedicated GPU leverages existing camera infrastructure, reducing the cost to a fraction while providing more flexible and customisable analytics.

With GigaGPU dedicated servers, you pay a flat monthly or hourly rate. An RTX 5090 server at £1.50-£4.00/hour handles 18-28 camera feeds with full vehicle tracking. Browse current rates on our GPU server pricing page.

For city-wide deployments, the RTX 6000 Pro tier centralises processing for all monitored junctions. Visit our use cases and model guides for more deployment strategies.