  In this work, we tackle the problem of crowd counting in images. We present a
Convolutional Neural Network (CNN) based density estimation approach to solve
this problem. Predicting a high resolution density map in one go is a
challenging task. Hence, we present a two branch CNN architecture for
generating high resolution density maps, where the first branch generates a low
resolution density map, and the second branch incorporates the low resolution
prediction and feature maps from the first branch to generate a high resolution
density map. We also propose a multi-stage extension of our approach where each
stage in the pipeline utilizes the predictions from all the previous stages.
Empirical comparison with the previous state-of-the-art crowd counting methods
shows that our method achieves the lowest mean absolute error on three
challenging crowd counting benchmarks: Shanghaitech, WorldExpo'10, and UCF
datasets.
