In recent years, video surveillance has become an integral part of computer vision research, addressing a variety of challenges in security, memory management and content extraction from video sequences. This paper introduces the Robust Object Detection using Fire Hawks Optimizer with Deep Learning (ROD-FHODL) technique, a novel approach designed specifically for video surveillance applications. Combining object detection and classification the proposed technique employs a two-step procedure. Utilizing the power of the Mask Region-based Convolutional Neural Network (Mask-RCNN) for object detection, we optimize its hyperparameters using the Fire Hawks Optimizer (FHO) algorithm to improve its efficacy. Our experimental results on the UCSD dataset demonstrate the significant impact of the proposed work. It achieves an extraordinary RUNNT of 1.34$$s on the pedestrian-1 dataset, significantly outperforming existing models. In addition, the proposed system surpasses in accuracy, with a pedestrian-1 accuracy rate of 97.45% and Area Under the Curve (AUC) values of 98.92%. Comparative analysis demonstrates the superiority of the proposed system in True Positive Rate (TPR) versus False Positive Rate (FPR) across thresholds. In conclusion, the proposed system represents a significant advancement in video surveillance, offering advances in speed, precision and robustness that hold promise for enhancing security, traffic management and public space monitoring in smart city infrastructure and other applications.

近年来，视频监控已成为计算机视觉研究的一个组成部分，解决了安全、内存管理和视频序列内容提取方面的各种挑战。本文介绍了使用 Fire Hawks Optimizer 和深度学习 (ROD-FHODL) 技术进行鲁棒目标检测，这是一种专为视频监控应用而设计的新颖方法。所提出的技术将对象检测和分类相结合，采用两步过程。利用基于 Mask Region 的卷积神经网络 (Mask-RCNN) 的强大功能进行目标检测，我们使用 Fire Hawks Optimizer (FHO) 算法优化其超参数，以提高其效率。我们在 UCSD 数据集上的实验结果证明了所提出的工作的重大影响。它实现了非凡的 RUNNT 1.34$$行人 1 数据集上的 s，显着优于现有模型。此外，所提出的系统在准确度方面也表现出色，行人 1 准确率达到 97.45%，曲线下面积 (AUC) 值达到 98.92%。比较分析证明了所提出的系统在跨阈值的真阳性率（TPR）与假阳性率（FPR）方面的优越性。总之，所提出的系统代表了视频监控领域的重大进步，在速度、精度和稳健性方面取得了进步，有望增强智慧城市基础设施和其他应用中的安全、交通管理和公共空间监控。