A Novel Optimization Algorithm for Smart Video Surveillance System and Change Object Detection

Welcome to the repository for my Bachelor's Final Year Project, where I conducted research and developed an optimization algorithm for a Smart Video Surveillance System (SVSS). This project demonstrates a significant contribution to the field of smart surveillance systems by addressing optimization challenges to enhance performance, efficiency, and accuracy. The research conducted for this project was later presented and published in an international conference in Spain.

Paper Link: https://link.springer.com/chapter/10.1007/978-3-031-48858-0_9

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Purpose

• Enhance real-time surveillance performance.
• Minimize computational costs and resource usage for large-scale deployments.
• Improve accuracy in detecting and tracking objects in complex environments.
• Provide a scalable solution for modern surveillance systems used in urban and industrial setups.

The project integrates computer vision techniques, optimization methods, and intelligent decision-making algorithms to achieve these goals.

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Features

The SVSS project includes the following key features:

1. Optimization Algorithm:

   • Developed a custom algorithm to optimize the processing pipeline of smart surveillance systems.
   • Balanced performance and resource consumption for real-time operations.

2. Object Detection and Tracking:

   • Integrated advanced computer vision techniques for detecting and tracking moving objects in video streams.

3. Scalability:

   • Designed for large-scale surveillance setups, ensuring efficient processing even under high workloads.

4. Research Contribution:

   • The algorithm was tested in various scenarios to demonstrate its effectiveness.
   • Research findings were validated and later published at an international conference.

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Tech Stack

The project utilizes the following technologies and tools:

• Programming Language: Python
• Computer Vision Library: OpenCV
• Machine Learning Frameworks: TensorFlow, Keras
• Optimization Algorithms: Custom algorithm based on heuristic and statistical methods
• Visualization Tools: Matplotlib for data visualization
• Development Environment: Jupyter Notebook
• Dataset: Custom and publicly available datasets for surveillance systems
• Deployment Tools: Flask for presenting results through a web-based interface (optional)

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Repository Contents

The repository is organized into the following directories:

• code/: Contains the implementation of the optimization algorithm and the SVSS pipeline.
• data/: Includes sample datasets and test cases used for evaluation.
• results/: Stores output images, videos, and performance metrics of the system.
• research/: Includes the research paper and conference presentation materials.

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Getting Started

To set up the project locally:

1. Clone the Repository:

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

2. Create a Virtual Environment:

   python -m venv venv

3. Activate the Virtual Environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install Dependencies:

   pip install -r requirements.txt

5. Run the Project:

   python main.py

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Usage

1. Place your surveillance video files in the data/input_videos directory.
2. Modify the configuration file (config.py) to adjust parameters such as frame rate, resolution, and optimization settings.
3. Run the main.py script to process the video using the SVSS pipeline.
4. View the output in the results folder.

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Research Contribution

This research was presented and published at an international conference in Spain. The paper highlights the following:

• The novel optimization approach for smart surveillance systems.
• Comparative analysis of the algorithm's performance against existing methods.
• Applications of the system in real-world scenarios, including traffic monitoring, public safety, and industrial automation.

For the detailed research paper, refer to the research/ folder.

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Future Work

The following extensions can be explored:

• Integration of deep learning models for enhanced object detection and tracking.
• Real-time deployment on edge devices using lightweight algorithms.
• Application in domains such as autonomous vehicles and smart cities.

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License

This repository is licensed under the MIT License. See the LICENSE file for more details.

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Acknowledgments

• Special thanks to my academic supervisors for their guidance.
• Gratitude to the conference organizers for providing a platform to share this research.
• Thanks to the open-source community for the tools and libraries used in this project.

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Thank you for exploring this repository! Feel free to star the repository if you find it useful.