Abba Sani, Bizi Ali Mohammed, Lee Jeong-A, Bakouri Souley, Crespo Maria Liz
Department of Computer Science, Faculty of Science, Gubi Campus, Abubakar Tafawa Balewa University, Along Ningi/Kano Road, P.M.B. 0248, Bauchi, Nigeria.
Computer Systems Laboratory, Department of Computer Engineering, Chosun University, Dongku SeoSukDong 375, Gwangju, 501-759, South Korea.
Heliyon. 2024 Jul 20;10(15):e34922. doi: 10.1016/j.heliyon.2024.e34922. eCollection 2024 Aug 15.
The concept of security is becoming a global challenge, and governments, stakeholders, corporate societies, and individuals must urgently create a reasonable protection mechanism for good. Therefore, a real-time surveillance system is essential for detection, tracking, and monitoring. Many studies have attempted to provide better solutions but more research and better approaches are essential. This study presents a real-time framework for object detection and tracking for security surveillance systems. The system has been designed based on approximate median filtering, component labeling, background subtraction, and deep learning approaches. The new algorithms for object detection, tracking, and recognition have been implemented using Python and integrated with C# programming languages for ease of use. A software application framework is designed, implemented, and evaluated. The experimental results based on MOT-Challenge performance metrics show that the proposed algorithms have much better performance in terms of accuracy and precision on the MOT15, MOT16, and MOT17 datasets compared to state-of-the-art approaches. This framework also provides an accurate and effective means of monitoring and recognizing moving objects. The software development, including the design of the framework user interfaces, is coded in the C# programming language and integrated with Python using Microsoft Visual Studio (2019 edition). The integration is performed to provide a convenient user interface and to enable the execution of the framework as a standard and standalone software application. Future studies will consider the dynamic scalability of the framework to accommodate different surveillance application areas in overcrowded scenarios. Multiple data sources are integrated to enhance the performance for different scene times, locations, and weather conditions. Furthermore, other object-detection techniques such as You Only Look Once (YOLO) and its variants shall be considered in future studies. These techniques allow the framework to adapt to complex situations in which security surveillance is challenging.
安全概念正成为一项全球性挑战，政府、利益相关者、企业界和个人必须紧急建立一个合理的良好保护机制。因此，实时监控系统对于检测、跟踪和监测至关重要。许多研究试图提供更好的解决方案，但更多的研究和更好的方法必不可少。本研究提出了一种用于安全监控系统的目标检测与跟踪实时框架。该系统基于近似中值滤波、组件标记、背景减除和深度学习方法进行设计。用于目标检测、跟踪和识别的新算法已使用Python实现，并与C#编程语言集成，以方便使用。设计、实现并评估了一个软件应用框架。基于MOT-Challenge性能指标的实验结果表明，与现有方法相比，所提出的算法在MOT15、MOT16和MOT17数据集上的准确性和精确性方面具有更好的性能。该框架还提供了一种准确有效的手段来监测和识别移动物体。软件开发，包括框架用户界面的设计，用C#编程语言编码，并使用Microsoft Visual Studio（2019版）与Python集成。进行集成是为了提供一个方便的用户界面，并使框架能够作为一个标准的独立软件应用程序执行。未来的研究将考虑框架的动态可扩展性，以适应拥挤场景中的不同监控应用领域。集成多个数据源以提高在不同场景时间、地点和天气条件下的性能。此外，未来的研究将考虑其他目标检测技术，如You Only Look Once（YOLO）及其变体。这些技术使框架能够适应安全监控具有挑战性的复杂情况
