Abstract
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.
Abstract (translated)
这项研究介绍了一种创新的安全增强方法，结合了先进的图像分析和软计算。重点是一个智能监控系统，通过分析服饰来检测受限制区域内的未经授权的人员。传统的 security 措施在监测未经授权的访问时面临挑战。利用 YOLOv8，一种先进的物体检测算法，我们的系统根据影片中的服饰来识别授权人员。该方法涉及在全面数据集中训练 YOLOv8 模型，确保在特定区域的精确识别。软计算技术增强了对于动态环境和不断变化的光线条件的适应性。这项研究对于图像分析和软计算领域都做出了贡献，提供了一种高级的安全解决方案。强调了基于一致性的异常检测，为受限制区域建立了一个稳固的安全系统基础。结果表明，基于 YOLOv8 的监控在确保敏感地点的安全方面具有巨大潜力。
URL
https://arxiv.org/abs/2404.00645
