Iterative Crowd Counting

Viresh Ranjan, Hieu Le, Minh Hoai; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 270-285


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 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.

Related Material

[pdf] [arXiv]
author = {Ranjan, Viresh and Le, Hieu and Hoai, Minh},
title = {Iterative Crowd Counting},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}