Multi-scale Generative Adversarial Networks for Crowd Counting

Jianxing Yang, Yuan Zhou, Sun Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations


We investigate generative adversarial networks as an effective solution to the crowd counting problem. These networks not only learn the mapping from crowd image to corresponding density map, but also learn a loss function to train this mapping. There are many challenges to the task of crowd counting, such as severe occlusions in extremely dense crowd scenes, perspective distortion, and high visual similarity between pedestrians and background elements. To address these problems, we proposed multi-scale generative adversarial network to generate high-quality crowd density maps of arbitrary crowd density scenes. We utilized the adversarial loss from discriminator to improve the quality of the estimated density map, which is critical to accurately predict crowd counts. The proposed multi-scale generator can extract multiple hierarchy features from the crowd image. The results showed that the proposed method provided better performance compared to current state-of-the-art methods.

Original languageEnglish (US)
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538637883
StatePublished - Nov 26 2018
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: Aug 20 2018Aug 24 2018

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other24th International Conference on Pattern Recognition, ICPR 2018

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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