This research introduces an innovative security enhancement approach,
employing advanced image analysis and soft computing. The focus is on an
intelligent surveillance system that detects unauthorized individuals in
restricted areas by analyzing attire. Traditional security measures face
challenges in monitoring unauthorized access. Leveraging YOLOv8, an advanced
object detection algorithm, our system identifies authorized personnel based on
their attire in CCTV footage. The methodology involves training the YOLOv8
model on a comprehensive dataset of uniform patterns, ensuring precise
recognition in specific regions. Soft computing techniques enhance adaptability
to dynamic environments and varying lighting conditions. This research
contributes to image analysis and soft computing, providing a sophisticated
security solution. Emphasizing uniform-based anomaly detection, it establishes
a foundation for robust security systems in restricted areas. The outcomes
highlight the potential of YOLOv8-based surveillance in ensuring safety in
sensitive locations.

该研究介绍了一种创新的安全增强方法，利用先进的图像分析和软计算技术。研究重点是一种智能监控系统，通过分析服装来检测受限区域内的未经授权者。基于 YOLOv8 的先进目标检测算法，我们的系统在闭路电视录像中基于服装识别授权人员。方法包括在详尽的统一模式数据集上训练 YOLOv8 模型，以确保在特定区域内进行精确识别。软计算技术增强了对动态环境和光照变化的适应性。该研究对图像分析和软计算做出了贡献，提供了一个复杂的安全解决方案。强调基于服装的异常检测，为受限区域的强大安全系统奠定了基础。研究结果凸显了基于 YOLOv8 的监控在确保敏感地点安全方面的潜力。