This thesis presents a systematic approach for calibrating a Pan-Tilt-Zoom network camera and integrating it with state-of-the-art object detection and depth estimation algorithms for distance measurement of autonomous agents. A calibration methodology was adopted to compute the intrinsic parameters of the camera across various zoom levels, addressing the non-linear variations induced by dynamic zoom adjustments. This calibration process is fundamental for establishing an accurate mapping between the three-dimensional world and the two-dimensional image plane of the camera. Subsequently, the study focuses on estimating the distance of autonomous agents by leveraging a dual-framework approach. Object detection is performed using YOLOv8, chosen for its balance between computational efficiency and detection accuracy in real-time applications.