安全概念正在成为全球性挑战,和政府,利益相关者,公司社团,个人必须紧急建立合理的保障机制。因此,实时监控系统对于检测至关重要,跟踪,和监测。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是必不可少的。本研究提出了用于安全监控系统的对象检测和跟踪的实时框架。系统设计了基于近似中值滤波,组件标签,背景减法,和深度学习方法。新的目标检测算法,跟踪,和识别已使用Python实现,并与C#编程语言集成,以便于使用。设计了一个软件应用框架,已实施,并进行了评估。基于MOT-Challenge性能指标的实验结果表明,与最先进的方法相比,所提出的算法在MOT15,MOT16和MOT17数据集上的准确性和精度方面具有更好的性能。该框架还提供了监测和识别运动物体的准确有效手段。软件开发,包括框架用户界面的设计,使用C#编程语言编码,并使用MicrosoftVisualStudio(2019年版)与Python集成。执行集成以提供方便的用户界面并使框架能够作为标准和独立的软件应用来执行。未来的研究将考虑框架的动态可扩展性,以适应过度拥挤场景中的不同监视应用领域。集成了多个数据源,以增强不同场景时间的性能,地点,和天气条件。此外,其他对象检测技术,如你只看一次(YOLO)及其变体应在未来的研究中考虑。这些技术允许框架适应安全监控具有挑战性的复杂情况。
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.