Counting and locating high-density objects using convolutional neural network

Abstract

This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object counting and locating method based on a feature map enhancement combined with a multi-sigma refinement of the confidence map. The proposed method was evaluated in two counting datasets: trees and cars. For the tree dataset, our method returned a mean absolute error (MAE) of 2.05, a root-mean-squared error (RMSE) of 2.87 and a coefficient of determination (R2) of 0.986. For the car dataset (CARPK and PUCPR+), our method was superior to state-of-the-art methods. In the these datasets, our approach achieved an MAE of 4.45 and 3.16, an RMSE of 6.18 and 4.39, and an R2 of 0.975 and 0.999, respectively. We conclude that the proposed method is suitable for dealing with high object-density, returning a state-of-the-art performance for counting and locating objects.