<![CDATA[Monitoring the distance between individuals in public spaces remains relevant for crowd management, operational safety, and risk mitigation in public service. Manual inspections are difficult to carry out consistently and in real time, thus requiring an automated machine vision-based system. This research proposes a safe distance monitoring pipeline that detects humans, calculates the proximity between individuals. Human detection is performed with YOLOv4 and the CSPResNeXt50 backbone, then the center of the bounding box is used as a position representation. The distance between centroids is calculated using Euclidean Distance and calibrated based on camera parameters, thus that it can be mapped to distance units. To obtain a stable training configuration, two optimizers (Adam and SGD) are compared at two learning rates (1E-2 and 1E-3). Test results show that Adam with a learning rate of 1E-3 provides the best performance, achieving 96% object detection accuracy. The distance calculation module achieves 90% accuracy in determining the actual and predicted distances in optimal camera installation scenarios, with an average processing speed of 60 fps. These findings demonstrate a fast and practical approach for camera-based public space safety. Additionally, the backbone comparison shows that CSPResNeXt50 is slightly superior to DarkNet50, making it suitable for lightweight implementation on edge devices in real-time.]]>
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