The existing object tracking, localization, measurement, and other technologies mostly concentrate on dual cameras or using single camera plus the non-visual sensor technology. These technologies are achieved by increasing the amount of data at the expense of lowing the processing speed to achieve precise localization of machine vision. If machine vision localization can be achieved without increasing the amount of data processing, then only the monocular ranging method can be used. Therefore, monocular ranging is obviously more challenging in actual research. Motivated by this, this paper proposes a novel object learned method based on monocular vision. According to the geometric model of camera imaging and the basic principle of converting analog signals to digital signals, we derive the relationship model between the object distance, object height, camera height, image resolution, image target size, and camera parameters. We theoretically prove the infinite solvability of “self-invariance” and the solvability of “self-change”, which provides a theoretical basis for the object tracking, localization and measurement based on monocular vision. The experimental results show the correctness of our theory.