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Iterative Crowd Counting

  • Viresh RanjanEmail author
  • Hieu Le
  • Minh Hoai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11211)

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.

Keywords

Crowd counting Density estimation Multi-stage CNN 

Notes

Acknowledgement

This work was supported by SUNY2020 Infrastructure Transportation Security Center. The authors would like to thank Boyu Wang for participating on the discussions and experiments related to an earlier version of the proposed technique. The authors would like to thank NVIDIA for their GPU donation.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceStony Brook UniversityStony BrookUSA

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