1. A method for determining a classification boundary between an object and a background, comprising: recognizing, using a trained classifier, each of a plurality of object images and each of a plurality of background images; classifying, using the trained classifier, each of the plurality of object images and each of the plurality of background images; determining a confidence value for each of the plurality of recognized and classified object images and for each of the plurality of recognized and classified background images; calculating a confidence probability density distribution function for an object in the plurality of object images, wherein the confidence probability density distribution function for the object in the plurality of object images is based on the confidence values determined for the plurality of object images; calculating a confidence probability density distribution function for a background in the plurality of background images, wherein the confidence probability density distribution function for the background in the plurality of background images is based on the confidence values determined for the plurality of background images; and determining a classification boundary between the object in the plurality of object images and the background in the plurality of background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the plurality of object images or the background in the plurality of background images, or the calculated confidence probability density distribution functions for both the object in the plurality of object images and the background in the plurality of background images; wherein the predefined model comprises a probability of the object in the plurality of object images and the background in the plurality of background images being incorrectly classified, and the probability meets a predetermined target value; and wherein the probability of the object in the plurality of object images and the background in the plurality of background images being incorrectly classified that meets the predetermined target value, is calculated by a formula: 
 min(α∫ T ∞ f b ( x ) dx+b∫ −∞ T f v ( x ) dx ), wherein min( ) represents a minimization operation, f v (x) is the confidence probability density distribution function for the object in the plurality of object images, f b (x) is the confidence probability density distribution function for the background in the plurality of background images, a represents a penalty factor for incorrectly recognizing the background in the plurality of background images, b represents a penalty factor for incorrectly recognizing the object in the plurality of object images, and T represents the classification boundary.