Object tracking is a fundamental algorithm in robotics and various other fields that involves estimating real world coordinates of objects from images. To achieve this, it is necessary to accurately determine the location and orientation of the capturing cameras. Typically, this process, known as “camera pose estimation,” has been manually performed and requires a meticulous calibration procedure. In this paper, we introduce a pipeline designed to estimate the camera pose for all available cameras based on different methods, that estimates object-camera distance. The first method utilizes well-known tags that must be positioned in places visible from all cameras. The second approach is semi-automatic method that detect people moving within a scene and estimate 3D coordinates. The third algorithm aims for full automation, computing depth maps derived from a single image. All these methods generate a cloud point in camera space used as input by an optimization algorithm that compute camera poses based on a metric that minimize a re-projection function. Finally, the entire process and some experimental cases are presented.