Object’s 3D Position Extraction using a Single Camera

Background: Images get segmented and labeled and contained objects on images are identified through many methods yet dismal work only available about the exact position extraction of object while it is very important in the future generations of fully autonomous systems which would indeed be tasked to identify and manipulate obstacles or various subjects without any human supervisions. Method: In this paper a method to apply neural networks algorithms to solve this issue without attending complicated calculations is introduced. Findings: The most important result of this work is that the fact that trained and designed network result in known transfer functions between the input and output vectors which can be easily emulated by the embedded computers which may not be able to perform far more complicated calculations of camera calibration matrixes. This is a major advantages for the robotic systems developers. Applications/Improvements: It seems that method has had quite an acceptable turnover but it should be considered that the test environment has been similar and at the same scale as the training environment.