Corpus ID: 236447493

Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework

@article{Song2021RethinkingCA,
  title={Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework},
  author={Qingyu Song and Changan Wang and Zhengkai Jiang and Yabiao Wang and Ying Tai and Chengjie Wang and Jilin Li and Feiyue Huang and Yang Wu},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.12746}
}
  • Qingyu Song, Changan Wang, +6 authors Yang Wu
  • Published 27 July 2021
  • Computer Science
  • ArXiv
Localizing individuals in crowds is more in accordance with the practical demands of subsequent high-level crowd analysis tasks than simply counting. However, existing localization based methods relying on intermediate representations (i.e., density maps or pseudo boxes) serving as learning targets are counter-intuitive and error-prone. In this paper, we propose a purely point-based framework for joint crowd counting and individual localization. For this framework, instead of merely reporting… Expand
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