Real-Time-Smart-Surveillance-System-with-Anomaly-Detection

This repository contains a simple, yet functional, prototype for a real-time human anomaly detection and tracking system in crowded environments. The system is designed to identify and follow individuals whose behavior deviates from a learned "normal" pattern.

The project is built on a two-stage pipeline:

1. Perception: Detect and track individuals in a video stream.
2. Analysis: Analyze their movement for anomalous behavior.

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Core Technologies

• Object Detection: YOLOv8, a state-of-the-art model for real-time object detection.
• Multi-Object Tracking: DeepSORT, a robust algorithm that maintains unique IDs for individuals across frames, even during occlusions.
• Anomaly Detection: An Autoencoder model, which is a neural network trained to recognize "normal" patterns. It identifies anomalies by failing to reconstruct a pattern it has never seen before, using the reconstruction error as an anomaly score.
• Development Framework: PyTorch for building and training the anomaly detection model.
• Video Processing: OpenCV for handling video I/O.

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Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

First, ensure you have Python 3.8+ installed on your system. It is highly recommended to use a virtual environment to manage project dependencies.

Create a virtual environment

python3 -m venv venv

Activate the virtual environment

On macOS/Linux:

source venv/bin/activate

On Windows:

venv\Scripts\activate

Installation

Clone this repository to your local machine:

git clone https://github.com/your-username/your-repository-name.git
cd your-repository-name

Install the required Python packages:

pip install -r requirements.txt

Usage

To run the end-to-end prototype, you will need a video file for testing. The code is configured to use a video file named your_video.mp4 in the project directory.

Download a video clip you like to test or you can use the live video footage.

Place the video file in your project's root directory.

Rename the video file to your_video.mp4 or update the cv2.VideoCapture('your_video.mp4') line in the main.py script.

Run the script from your terminal:

python main.py

The system will open a video window displaying the real-time detection, tracking, and anomaly status for each person. Press q on your keyboard to exit the program.

Project Structure

main.py: The main script that runs the end-to-end prototype.

requirements.txt: Lists all Python dependencies.

.gitignore: Specifies files to be ignored by Git.

README.md: Project description and instructions.

##Visuals