Abstract. 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