Prediction of XYZ coordinates from an image using mono camera

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Estimating the position of a homogeneous object from an image for XY position is quite simple because it has the same dimensions XY. However, determining the XYZ position requires a unique approach. Generally, for estimating 3D position, stereo camera or expensive cameras are used with complicated computer vision algorithms. In this paper, we classify the position of an object using a mono camera. The image is divided into 3185 classes and five layers as a machine learning algorithm references. The k-nearest neighbors (kNN) approach usually is to find the closest point of the centroids to the closest class. Thus, this approach can be used as a three-axis prediction method that can afford the best performance solution.