CASCADED NEURAL NETWORK WITH SCALE DEPENDENT POOLING FOR OBJECT DETECTION

摘要

A computer-implemented method for training a convolutional neural network (CNN) is presented. The method includes receiving regions of interest from an image, generating one or more convolutional layers from the image, each of the one or more convolutional layers having at least one convolutional feature within a region of interest, applying at least one cascaded rejection classifier to the regions of interest to generate a subset of the regions of interest, and applying scale dependent pooling to convolutional features within the subset to determine a likelihood of an object category.