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arXiv · 1807.09959

Iterative Crowd Counting

Viresh Ranjan·Hieu Le·Minh Hoai
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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.

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Viresh Ranjan, Hieu Le, Minh Hoai. 2018-07-26. Iterative Crowd Counting. https://arxiv.org/abs/1807.09959

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