Iterative Crowd Counting
This addresses the problem of accurate crowd density estimation for applications like surveillance and event management, representing an incremental improvement over existing methods.
The paper tackles crowd counting in images by proposing a two-branch CNN architecture that generates high-resolution density maps through iterative refinement, achieving the lowest mean absolute error on three challenging benchmarks: Shanghaitech, WorldExpo'10, and UCF datasets.
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.