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Real-time object detection, tracking, and monitoring framework for security surveillance systems
Sani Abba1, Ali Mohammed Bizi1, Jeong-A Lee2
1Department of Computer Science, Faculty of Science, Gubi Campus, Abubakar Tafawa Balewa University, Along Ningi/Kano Road, P.M.B. 0248, Bauchi, Nigeria.
Heliyon
|August 15, 2024
Summary
This study introduces a real-time object detection and tracking framework for enhanced security surveillance. The system demonstrates superior accuracy and precision on benchmark datasets, improving real-time monitoring capabilities.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Security Systems
Background:
- Global security challenges necessitate advanced protection mechanisms.
- Real-time surveillance is crucial for detection, tracking, and monitoring.
- Existing solutions require further research and improved approaches.
Purpose of the Study:
- To present a real-time framework for object detection and tracking tailored for security surveillance.
- To develop and evaluate novel algorithms for object detection, tracking, and recognition.
- To create an integrated software application for practical use.
Main Methods:
- Utilized approximate median filtering, component labeling, and background subtraction.
- Incorporated deep learning approaches for object detection and recognition.
- Implemented algorithms in Python and integrated with C# for a user-friendly application.
Main Results:
- The proposed framework achieved superior performance in accuracy and precision on MOT15, MOT16, and MOT17 datasets.
- Demonstrated effective real-time monitoring and recognition of moving objects.
- The integrated software application provides a convenient user interface and standalone functionality.
Conclusions:
- The developed framework offers an accurate and effective solution for security surveillance.
- Future work will focus on dynamic scalability and integration of diverse data sources for varied conditions.
- Exploring advanced object detection techniques like YOLO will enhance adaptability in complex scenarios.
