STSMS is an AI-powered system designed to enhance street safety , ambulance prioritization and traffic control using computer vision and machine learning. It integrates real-time surveillance and automated decision-making for improved urban management.

• AI-powered accident , threat , face recongnition and women safety management system.

• Detects ambulances at intersections.
• Adjusts traffic signals dynamically to prioritize emergency vehicles to save lives.

• Analyzes vehicle density using AI-driven traffic monitoring.
• Optimizes signal timing to reduce congestion and improve traffic flow.

• Computer Vision & AI: OpenCV, Deep Learning Models.
• Incident Detection: LLM with Flask API.
• Traffic & Ambulance Detection: YOLO-based object detection.
• Backend: Flask API for real-time processing.
• Database: PostgreSQL / MySQL for incident and vehicle data storage.

The Street Safety Management System is an AI-powered solution designed to enhance urban safety by monitoring streets through cameras, recognizing faces using *OpenCV, and detecting incidents using *a Large Language Model (LLM) with Flask. It integrates real-time surveillance, face matching, and an automated alert system to notify law enforcement and the public about emergencies.

• Face Recognition: OpenCV and Deep Learning Models (Python-based real-time detection and matching)
• Incident Detection: LLM integrated with Flask (AI-based classification of incidents)
• Backend: Flask for API development
• Database: MySQL for storing wanted persons and incident reports

• Camera Capture: Captures live footage from street cameras.
• Captured Frame: Stores individual frames extracted from video footage.
• Frame Storage: Saves frames for further analysis by the system.

• Face Detection: Uses OpenCV to detect human faces in captured frames.
• Feature Extraction: Extracts facial features using OpenCV's deep learning models.
• Face Matching: Compares detected faces with a database of wanted persons.
• Wanted Person DB: Contains records of individuals flagged as wanted.
• Police Alert System: Triggers alerts to law enforcement if a match is found.

• Image Preprocessing: Enhances image quality for better analysis.
• Incident Classification Model: Uses an LLM to classify incidents (e.g., fights, accidents, theft, women harassment).
• Flask API Integration: A REST API built with Flask connects the LLM to the system for real-time processing.
• Incident Type: Determines the nature of the detected incident.
• User Alert System: Notifies Emergency Services based on the incident type.

• Emergency Alert Successful: Confirms that the alert has been successfully sent to authorities.

1. Camera Module captures real-time footage and extracts frames.
2. Frames are analyzed using OpenCV-based Face Recognition to identify known suspects.
3. The same frames are processed by the LLM-powered Incident Detection Module via Flask API to classify events.
4. If a wanted suspect is found or an incident is detected, the Police Alert System is triggered.
5. Alerts are sent to the *Control Center, nearby law enforcement via the *Alert & Response System.

git clone https://github.com/apoorv1110/STSMS.git
cd Street-Safety-Management-System

python3 llm_service.py

python3 judging_service/run_judge.py

python3 LLM.py

python3 capture_service/run_capture.py

• The camera captures images in real time.
• The images are matched against the wanted database.
• The LLM service processes the image for incident detection.
• The system returns results from both modules and triggers alerts if necessary.

✅ Real-time Face Recognition with OpenCV – Identifies wanted persons instantly.

✅ AI-powered Incident Detection with LLM + Flask – Classifies incidents in real-time.

✅ Automated Police Alerts – Notifies law enforcement of emergencies.

✅ REST API Integration – Ensures smooth communication between different modules.

• Expanded Missing Persons Database – To assist in rescue operations.

The Ambulance Management Module ensures emergency vehicle prioritization at intersections using AI-based detection and real-time traffic signal control.

• Ambulance Detection: YOLO-based object detection.
• Traffic Signal Control: AI-driven dynamic light adjustment.
• Communication Module: Flask API for signal updates.

• Captures live traffic footage.
• Uses AI to detect ambulances in real time.

• Adjusts signals dynamically to allow ambulance passage.
• Holds non-emergency vehicles for 10 seconds.
• Notifies upcoming intersections to clear the path.

• Updates traffic status via a centralized server.
• Uses Flask API for real-time signal adjustments.

1. Camera Module detects ambulances in real-time using YOLO.
2. Traffic Signal Control System adjusts lights accordingly.
3. Nearby intersections receive alerts to prepare for the ambulance.
4. Non-emergency vehicles are held for 10 seconds.

cd Ambulance_management-System
python3 ambulance_detector.py

✅ Real-time Ambulance Detection – Instantly identifies ambulances.

✅ Automated Signal Adjustment – Prioritizes emergency vehicles.

✅ Path Clearance System – Holds non-emergency vehicles temporarily.

✅ API-based Integration – Ensures smooth system communication.

The Traffic Signal Control Module dynamically adjusts traffic lights based on real-time vehicle density and emergency vehicle detection. It optimizes traffic flow, reduces congestion, and ensures smooth passage for ambulances.

• Traffic Density Analysis: AI-based monitoring using OpenCV and YOLO.
• Signal Timing Optimization: Dynamic adjustments using ML models.
• Emergency Vehicle Priority: Integrated with Ambulance Management System.
• Backend: Flask API for real-time updates.
• Database: PostgreSQL / MySQL for traffic data storage.

• Analyzes real-time traffic density.
• Detects emergency vehicles and adjusts signals accordingly.

• Adjusts light durations based on congestion levels.
• Extends green signals for ambulances.
• Reduces wait times at busy intersections.

1. Camera Module captures real-time traffic footage.
2. AI-powered Signal Optimization analyzes traffic density and emergency vehicle presence.
3. Signal Timing Adjustment dynamically modifies traffic light durations.
4. Emergency Priority Activation extends green signals for ambulances.
5. Live Updates Sent to other intersections to ensure smooth traffic flow.

cd Traffic Management System
python3 main.py

✅ AI-powered Traffic Monitoring – Real-time congestion analysis.

✅ Dynamic Signal Adjustment – Reduces congestion and improves flow.

✅ Emergency Vehicle Priority – Ensures smooth passage for ambulances