How coderyasa089/trafficsense works
TrafficSense processes video feeds through detection, tracking, and violation rules, then captures evidence and submits violation records to a FastAPI backend. The backend stores records in SQLite and supports retrieval and ticket generation; the README also describes a dashboard and coordinated multi-camera processing. The backend and AI path are supported by sampled source. The dashboard implementation was not sampled, so its connection to backend APIs is not asserted.
Main entry points: main_engine.py Traffic Engine · main.py REST API · api_client.py Violation API Client · multi_cam.py Multi-Camera Runner
Components 12
Camera Processing
- Multi-Camera Runner
ai_engine/multi_cam.py - Traffic Engine
ai_engine/main_engine.py
Violation Analysis
- Vehicle Detector
ai_engine/core/detector.py - Object Tracker
ai_engine/core/tracker.py - Violation Rules
ai_engine/core/rule_engine.py - Evidence Capture
ai_engine/evidence/evidence.py
Backend Services
- Violation API Client
ai_engine/utils/api_client.py - REST API
backend/main.py
Violation Records
- Violation Store
backend/database.py - Ticket Generation
backend/main.py
Dashboard
- HTML Dashboard
frontend/index.html
Other components
- Video Feeds
Connections 10
- Video Feeds → Traffic Engine: provides frames
ai_engine/main_engine.py - Multi-Camera Runner → Traffic Engine: starts per feed
ai_engine/multi_cam.py - Traffic Engine → Vehicle Detector: detects objects
ai_engine/main_engine.py - Traffic Engine → Object Tracker: updates tracks
ai_engine/main_engine.py - Traffic Engine → Violation Rules: evaluates tracks
ai_engine/main_engine.py - Traffic Engine → Evidence Capture: saves snapshots
ai_engine/main_engine.py - Traffic Engine → Violation API Client: queues violations
ai_engine/main_engine.py - Violation API Client → REST API: posts reports
ai_engine/utils/api_client.py - REST API → Violation Store: reads and writes
backend/main.py - REST API → Ticket Generation: generates tickets
backend/main.py