Method for estimating a crowd counting, a method for training a model for estimation of the crowd counting, and an electronic device for performing the same

A non-transitory computer-readable recording medium on which a computer program executed by a computer is recorded, the computer program comprising:

• receiving a first model for a crowd counting estimation that is trained based on a data set, wherein the data set includes an image and a reference annotation corresponding to a human object in the image and includes a sample that is the reference annotation having an error;
• receiving a second model for a crowd counting estimation that is trained by correcting a portion of the reference annotation included in the data set during each training epoch;
• receiving a target image;
• generating a first crowd counting predicting a number of crowds present in the target image from the target image through the first model and a second crowd counting predicting a number of crowds present in the target image from the target image through the second model;
• and
• outputting crowd counting information in a form of a range on the basis of the first crowd counting and the second crowd counting, and
• wherein the portion of the reference annotation to be corrected is selected based on a learning difficulty which is calculated based on a loss value for each first pixel between the reference annotation which is obtained for each training epoch in a training process for the first model and a first predictive annotation which is predicted though the first model.

An electronic device comprising:

• a transmission and reception unit configured to receive a target image;
• and
• a processor configured to estimate a crowd counting on the basis of the target image,
• wherein the processor is configured to:
• receive a first model for a crowd counting estimation that is trained based on a data set, wherein the data set includes an image and a reference annotation corresponding to a human object in the image and includes a sample that is the reference annotation of which at least a portion has an error;
• receive a second model for a crowd counting estimation that is trained by correcting the portion of the reference annotation included in the data set during each training epoch;
• generate a first crowd counting predicting a number of crowds present in the target image from the target image through the first model;
• generate a second crowd counting predicting a number of crowds present in the target image from the target image through the second model;
• and
• output crowd counting information in a form of a range on the basis of the first crowd counting and the second crowd counting, and
• wherein the portion of the reference annotation to be corrected is selected based on a learning difficulty which is calculated based on a loss value for each first pixel between the reference annotation which is obtained for each training epoch in a training process for the first model and a first predictive annotation which is predicted though the first model.