Method for generating training data on basis of deformable gaussian kernel in population counting system

Abstract

Disclosed is a method for generating training data based on a deformable Gaussian kernel in a crowd counting system, which includes steps of: finding a set of overlapping Gaussian kernels from training data; stretching an occluded Gaussian kernel; rotating the occluded Gaussian kernel; adjusting a center point coordinate of the occluded Gaussian kernel; and determining whether there is any Gaussian kernel that have not been selected yet in the training data, and outputting a resulted crowd density map having gray values as training data. Therefore, feature similarity between the crowd density map in the training data and an actual picture is effectively increased so that a regular pattern between the training data and the actual picture can be more readily learned by a convolutional neural network and an accuracy of the crowd counting system is improved.