This work focuses on the development of a real-time 3D pose estimation system for tracking human movements. Pose estimation is vital in computer vision and has applications in entertainment, sports, healthcare and robotics. The challenge of estimating 3D poses involves identifying the body s major joints and their spatial relationships, creating a skeletal representation of human figures, which is complicated by factors such as occlusions, variations in camera point of view, and close interactions between individuals. The project proposes a low cost solution even though this may result in a decrease in overall system accuracy, it provides an alternative to industry standard systems used in motion capture. We used an arbitrary number of cameras (n≥4) to minimize projection and tracking errors caused by target occlusions. The approach adopted in this project involves a reconstruction logic, as seen in fig 1-a. The Python programming language is used for image processing, with the help of the OpenCV library for 3D pose reconstruction. The camera chosen for the project is Intelbras VIP1430 B G2, and Google s MediaPipe open source implementation is used for 2D pose estimation. The limitations of this approach and possible improvements are also discussed, providing a solid basis for future research and applications in the field.