Camera calibration and its application of binocular stereo vision based on artificial neural network

The trajectory tracking system of particle motion on sieve surface was designed by the combination of the analysis of image sequences based on binocular stereo vision and three-dimensional position reconstruction based on artificial neural network. Firstly, the calibration plane with uniformly distributed solid circles was placed in multiple positions within the effective field of view. The images of the calibration plane in each position can be captured by the binocular stereo vision system. Then, after image processing, the two-dimensional coordinates of the center of the circles were used as the input sample set for training. The artificial neural network was used to establish an implicit vision model. By this model, the three-dimensional position of the materials can be acquired without any complex camera calibration operation. Lastly, experiments showed that the proposed scheme is feasible, which will provide a good basis for further research.