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Procedia Computer Science
Article . 2019 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
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Procedia Computer Science
Article
License: CC BY NC ND
Data sources: UnpayWall
DBLP
Conference object . 2025
Data sources: DBLP
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Feature Pyramid Networks for Crowd Counting

Authors: Tjeng Wawan Cenggoro; Ayu Hidayah Aslamiah; Ardian Yunanto;

Feature Pyramid Networks for Crowd Counting

Abstract

Abstract In this paper, we propose the use of Feature Pyramid Networks (FPN) for Crowd Counting problem. FPN previously has been used for retinanet, the state-of-the-art model for object detection. By using FPN, our proposed crowd counting model achieved a state-of-the-art performance for UCF CC 50 dataset with MAE 136.4 and MSE 223.6. The proposed model is also achieved a state-of-the-art MSE value of 7.6 for ShanghaiTech Part B dataset. The code can be accessed at https: //github.com/wawancenggoro/fpncc.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Top 10%
Top 10%
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