Indirect calibration can build a calibration model and has been used by many academic researchers. For most methods

the detection field of binocular stereo vision system has been regarded as a cuboid space to make the sample set. However

the main problem existing in the method of sample is that the detection field of binocular stereo vision system is conical space. Predicting the label of instance

which is absent in sampling area

is also difficult. The calibration model is usually not a clear mathematical model in these studies. This paper proposes a calibration method by support vector machine (SVM) based on binocular system full vision field sampling to improve the video camera's insufficient and indirect calibration sampling and vague model expression

and realize the complete modal sampling of detection view field in small vision field. The sampling method uses the readability of the target number of hexagonal lattice calibration board as the basis

collects parallax coordinates and world coordinates

and establishes complete sample sets in the entire effective vision field of the binocular system. The SVM is selected to train the sample set

and a calibration with mathematical expression is established with model parameters calculated from the SVM algorithm to the decision function. Five mutually non-centrosymmetric polygons are placed at the four corners and center of the hexagonal lattice calibration board. The position information of the calibration board can be obtained with the collected partial region information of the calibration board

and the target number is known. The target number in images is acquired by HALCON operators by moving the calibration plate

and a complete set of samples of detection field is established based on binocular system. Finally

the SVM algorithm is used to train the samples to obtain calibration models that can articulate the mathematical form of a calibration. The samples used in this study include 568 targets from all 2 839 samples

and contain ground truths in the form of accurate world coordinates for detected targets. Two kinds of sampling method are adopted in the experiment

namely

traditional sampling method for cuboid space and full vision field sampling method for conical space. Experimental results show that calibrated error was reduced by 24.51% as compared with that of the model established based on traditional sampling method. Thus

the feasibility of the proposed method was supported given that the calibration error of calibration model was reduced in the unsampled region with conventional method. A calibration method by SVM based on binocular system full vision field sampling is proposed with non-centrosymmetric polygons to identify the target number on calibration board. The proposed method improves the indirect calibration precision. The method is also feasible

robust

and suitable to indirect calibration based on binocular system in small vision field.