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2018 | OriginalPaper | Buchkapitel

Iterative Crowd Counting

verfasst von : Viresh Ranjan, Hieu Le, Minh Hoai

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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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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Titel: Iterative Crowd Counting
verfasst von: Viresh Ranjan
Hieu Le
Minh Hoai
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01233-5

Electronic ISBN: 978-3-030-01234-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01234-2_17

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