🚨 SecureScope Autonomous Surveillance System 🛡️👁️

🌟 Project Overview 🚀

Welcome to SecureScope – an advanced autonomous surveillance system that leverages AI and machine learning (ML) to deliver real-time video monitoring and intelligent analysis. This system detects suspicious activities, performs facial recognition, and sends instant alerts—powered by a sleek and intuitive web interface.

📸 Screenshots:

🏠 Home Page Before Execution:

View of the home page before activating the surveillance system:

🖥️ Home Page After Execution:

The home page after activating the surveillance system:

🚨 Alert Page:

This is the alert page where real-time notifications of suspicious activity are shown:

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🔑 Key Features:

• 🎥 Real-time Surveillance Streaming: Stream live video and detect objects in real-time.
• 🤖 Autonomous Object Tracking: Track moving objects automatically in the camera's field of view.
• 🧑‍⚖️ Facial Recognition: Recognize and track individuals from your surveillance footage.
• 📲 Real-time Alerts: Receive instant notifications for suspicious activities or unauthorized individuals.
• 🧠 Machine Learning Integration: Continuously enhances detection accuracy through machine learning algorithms.
• 🔒 Data Encryption: Securely store all surveillance data with encryption to ensure privacy.
• 🖥️ User Interface: A sleek, responsive web-based interface for smooth monitoring and control.

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🛠️ Technologies Used:

💻 Frontend:

• HTML, CSS, JavaScript – To create a dynamic and interactive user interface and live video streaming.
• Bootstrap – For a mobile-responsive and sleek frontend design.

🔙 Backend:

• Python – Flask/Django for handling server-side logic and video processing.
• OpenCV – For real-time object detection and tracking.
• TensorFlow & Keras – For implementing machine learning models for facial recognition and behavior analysis.
• SQLite / PostgreSQL – For secure and structured data storage.

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⚙️ How It Works:

SecureScope utilizes live video feeds processed by machine learning algorithms to detect objects, recognize faces, and identify suspicious activities. The system analyzes the footage in real-time, sending alerts whenever an anomaly is detected.

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🛠️ Basic Workflow:

1. 📹 Video Feed: Stream live footage from your surveillance cameras.
2. 🔍 Object Detection: ML algorithms automatically detect and track objects in real-time.
3. 🧑‍⚖️ Facial Recognition: Compares faces in the video to a database of known individuals.
4. ⚠️ Anomaly Detection: Detects abnormal behaviors or events that deviate from the norm.
5. 🚨 Real-time Alerts: Instantly notifies users via email or message when suspicious behavior is detected.

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🚦 Getting Started:

📋 Prerequisites:

Ensure the following are installed on your machine:

• Python 3.x 🐍
• pip (Python's package installer) 📦
• Node.js 💻 (For frontend dependencies)
• Git 🔧 (For version control)

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🔧 Installation Steps:

1. Clone the repository:

Start by cloning the repository to your local machine:

git clone https://github.com/yourusername/SecureScope.git
cd SecureScope

2. Set up Python environment:

Next, create a virtual environment for Python:

python -m venv venv

Activate the virtual environment:

• On Windows:

  venv\Scripts\activate

• On macOS/Linux:

  source venv/bin/activate

3. Install Python dependencies:

Install all necessary Python libraries with the following command:

pip install -r requirements.txt

4. Set up the frontend:

Install Node.js dependencies to set up the frontend:

npm install

5. Start the server:

Start the Python server to launch the system:

python app.py

6. Access the system:

Open your web browser and navigate to http://localhost:5000 to start using SecureScope.

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🔒 Security:

All surveillance data is encrypted to ensure privacy and data protection. Additionally, the system uses secure protocols to transmit alerts and data.

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📄 License:

This project is licensed under the MIT License – see the LICENSE file for more details.

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💡 Contributing:

We welcome contributions to SecureScope! Feel free to open issues or pull requests if you'd like to contribute. For more details, refer to the CONTRIBUTING.md.

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