Internet of things (IoT) is transforming technological evolution in several practical applications. These applications range from smart cities, smart healthcare to intelligent video surveillance, where the primary interest is person monitoring and detection. The amalgamation of Artificial Intelligence (AI) and IoT-based techniques maintain a balance between computational cost and efficiency that is essential for next-generation IoT networks. In this context, a real-time IoT-enabled people detection system is introduced. The developed system performs image processing task over the cloud using an internet connection, thus reduces the computational cost by processing high-resolution images over the cloud. For person detection, a pre-trained Cascade RCNN, a deep learning approach is used. It is an object detection architecture, seeks to address discrediting performance with increased Intersection over Union (IoU) thresholds. As the architecture is pre-trained with COCO data set and the person body’s appearance in overhead perspective is significantly different; thus, additional training is performed to enhance the detection results. Taking advantage of transfer learning architecture is trained for overhead person images, and the newly trained feature layer is added to the existing architecture. Experimental outcomes reveal that additional training increases the detection architecture’s performance with an accuracy rate of 0.96.

物联网（IoT）正在改变几种实际应用中的技术发展。这些应用范围从智能城市，智能医疗保健到智能视频监控，其中主要的关注点是人员监视和检测。人工智能（AI）和基于IoT的技术的融合在计算成本和效率之间保持了平衡，这对于下一代IoT网络至关重要。在这种情况下，引入了一种支持物联网的实时人员检测系统。开发的系统使用Internet连接在云上执行图像处理任务，从而通过在云上处理高分辨率图像来降低计算成本。对于人员检测，使用了预先训练的Cascade RCNN，它是一种深度学习方法。它是一种对象检测架构，力图通过提高联合路口（IoU）阈值来解决信誉不良的问题。由于该架构已使用COCO数据集进行了预训练，并且从俯视角度看人体的外观有很大不同。因此，执行额外的训练以增强检测结果。利用转移学习体系结构来训练高空人员图像，并将新训练的要素层添加到现有体系结构中。实验结果表明，额外的训练可以提高检测体系结构的性能，准确率达到0.96。进行额外的培训以增强检测结果。利用转移学习体系结构来训练高空人员图像，并将新训练的要素层添加到现有体系结构中。实验结果表明，额外的训练可以提高检测体系结构的性能，准确率达到0.96。进行额外的培训以增强检测结果。利用转移学习体系结构来训练高空人员图像，并将新训练的要素层添加到现有体系结构中。实验结果表明，额外的训练可以提高检测体系结构的性能，准确率达到0.96。