Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization

Yi Wang, Xinyu Hou, Lap Pui Chau

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

12 Citations (Scopus)

Abstract

In this paper, we propose a simple yet effective crowd counting and localization network named SCALNet. Unlike most existing works that separate the counting and localization tasks, we consider those tasks as a pixel-wise dense prediction problem and integrate them into an end-To-end framework. Specifically, for crowd counting, we adopt a counting head supervised by the Mean Square Error (MSE) loss. For crowd localization, the key insight is to recognize the keypoint of people, i.e., the center point of heads. We propose a localization head to distinguish dense crowds trained by two loss functions, i.e., Negative-Suppressed Focal (NSF) loss and False-Positive (FP) loss, which balances the positive/negative examples and handles the false-positive predictions. Experiments on the recent and large-scale benchmark, NWPU-Crowd, show that our approach outperforms the state-of-The-Art methods by more than 5% and 10% improvement in crowd localization and counting tasks, respectively. The code is publicly available at https://github.com/WangyiNTU/SCALNet.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781665449892
DOIs
Publication statusPublished - Jul 2021
Externally publishedYes
Event2021 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2021 - Shenzhen, China
Duration: 5 Jul 20219 Jul 2021

Publication series

Name2021 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2021

Conference

Conference2021 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2021
Country/TerritoryChina
CityShenzhen
Period5/07/219/07/21

Keywords

  • convolutional neural network (CNN)
  • Crowd counting
  • crowd localization
  • dense prediction
  • keypoint estimation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Media Technology
  • Control and Optimization

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