Iterative Crowd Counting

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.

arXiv commentECCV 2018

References in corpus (1)

• CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

Cited by in corpus (6)

• Learning from Synthetic Data for Crowd Counting in the Wild
• Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting
• Mask-aware networks for crowd counting
• CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting
• Pixel-wise Crowd Understanding via Synthetic Data
• Recurrent Distillation based Crowd Counting