Security Plus

Overview

Security Plus specializes in AI-powered surveillance technology designed to enhance real-time security. Our state-of-the-art software continuously monitors live video feeds, identifying criminal activities such as theft, violence, or suspicious behavior.

Features

• AI-Powered Monitoring: Uses advanced machine learning algorithms to detect threats and anomalies.
• Real-Time Analysis: Continuously analyzes video feeds for security risks.
• Instant Alerts: Notifies authorities immediately upon detecting unlawful activities.
• Proactive Security: Helps safeguard communities, businesses, and public areas.

How It Works

1. The system processes live video feeds in real-time.
2. AI algorithms analyze the footage to detect potential threats.
3. If a threat is identified, an alert is sent to the concerned authorities.
4. Authorities can take swift action to prevent or mitigate risks.

Why Choose Security Plus?

• Enhanced Crime Prevention: Stay ahead of threats with proactive security measures.
• Automated Surveillance: Reduces the need for manual monitoring.
• Scalable Solutions: Suitable for businesses, public spaces, and communities.

Stay one step ahead of crime with Security Plus – the intelligent surveillance system built for modern security needs.

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Running the Application with Docker

Prerequisites

• Docker
• Docker Compose

Running the Application

1. Clone the Repository

   git clone "https://github.com/Sarthacker/Security-plus.git"
   cd security-plus

2. Build and Run Containers

   docker-compose up --build

3. Access the Application

   • Backend: http://localhost:5000
   • Frontend: http://localhost:3000

4. Stopping the Application

   docker-compose down

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Running the Flask Backend (Without Docker)

Prerequisites

• Python (>=3.8)
• pip (latest version recommended)
• Virtual environment (optional but recommended)

Installation

1. Create and Activate Virtual Environment (Optional but Recommended)

   python -m venv venv
   source venv/bin/activate  # macOS/Linux
   venv\Scripts\activate  # Windows

2. Install Dependencies

   cd backend
   pip install -r requirements.txt

3. Run the Flask Server

   python app.py

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Running the React Frontend (Without Docker)

Prerequisites

• Node.js (>=14.0)
• npm or yarn

Installation and Running

1. Navigate to the Frontend Directory

   cd frontend

2. Install Dependencies

   npm install  # or yarn install

3. Start the React Development Server

   npm start  # or yarn start

4. Access the Application

   • Frontend: http://localhost:3000

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Dataset

• The dataset used to train our model is the UCF Crime Dataset available on Kaggle.
• The Model Weights can be downloaded from here.