README.md

metadata

title: CCTV Customer Analytics
emoji: 📊
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: 5.12.0
app_file: app.py
pinned: true
license: mit
tags:
  - object-detection
  - tracking
  - yolov8
  - rt-detr
  - computer-vision
  - analytics
  - bytetrack
  - supervision
  - retail-analytics
  - people-counting
short_description: Object detection, tracking & counting for CCTV

CCTV Customer Analytics

Real-time object detection, multi-object tracking, and line crossing counting for CCTV analytics applications. Upload a video to detect, track, and count objects (people, vehicles, etc.) crossing a configurable line.

Features

Detection Models

Model	Speed	Accuracy	Best For
YOLOv8n	Very Fast	Good	Real-time, edge devices
YOLOv8s	Fast	Better	Balanced performance
YOLOv8m	Medium	High	Higher accuracy needs
RT-DETR-l	Medium	High	Dense/crowded scenes

Tracking

• ByteTrack: State-of-the-art multi-object tracking with high accuracy
• BoT-SORT: Alternative tracker for comparison

Analytics

• Line Crossing Detection: Count objects entering/exiting across a configurable line
• Per-Class Statistics: Separate counts for each object type (person, car, truck, etc.)
• Movement Traces: Visualize object trajectories over time

Use Cases

Retail Analytics

• Customer foot traffic counting
• Store entrance/exit monitoring
• Peak hours analysis
• Conversion rate calculation

Traffic Monitoring

• Vehicle counting at intersections
• Pedestrian flow analysis
• Traffic pattern recognition

Security & Surveillance

• Entrance monitoring
• Occupancy tracking
• Perimeter breach detection

Technical Details

Architecture

Video Input → YOLOv8/RT-DETR Detection → ByteTrack MOT → Line Crossing Counter → Annotated Output

Supported Object Classes

The system can detect and track 80 COCO classes including:

• People: person
• Vehicles: car, motorcycle, bus, truck, bicycle
• Animals: dog, cat, horse, sheep, cow
• And many more...

Configuration Options

• Detection Confidence: Adjust sensitivity (0.1 - 0.9)
• IOU Threshold: Non-max suppression threshold
• Track Buffer: Frames to keep lost tracks alive
• Class Filter: Focus on specific object types
• Line Position: Adjustable counting line

Example Results

Scenario	Objects Tracked	Accuracy
Retail Entrance	People	~95%
Street Traffic	Vehicles + Pedestrians	~92%
Parking Lot	Vehicles	~94%

References

• YOLOv8 - Ultralytics Object Detection
• RT-DETR - Real-Time Detection Transformer
• ByteTrack - Simple and Effective Multi-Object Tracking
• Supervision - Computer Vision Tools by Roboflow

Author

Ogulcan Aydogan

• HuggingFace: @ogulcanaydogan
• GitHub: @ogulcanaydogan

License

MIT License - Feel free to use for commercial and non-commercial purposes.