Multi-level Attention Network for Accurate Crowd Counting in Challenging Rail Transit Environment

Jianfan Wu; Zhengyu Xie; Yong Qin; Jiali Wang

doi:10.1007/s11633-025-1581-1

February 1, 2026Machine Intelligence Research

Multi-level Attention Network for Accurate Crowd Counting in Challenging Rail Transit Environment

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Key Points

Accurate crowd counting is achieved using a multi-level attention network, enhancing identification of dense crowds.
The model shows a remarkable performance with a mean absolute error of 3.5 people in crowded scenarios.
Evaluation utilized a dataset from various rail transit environments characterized by diverse crowd behaviors and patterns.
Improvements in accuracy may enable better safety management in public transportation systems, particularly during peak times.

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Implication

Observational analysis demonstrates improved crowd counting accuracy in rail transit environments, suggesting effective methods for density estimation.

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Wu et al. (2026) studied this question.

synapsesocial.com/papers/69a7665cbadf0bb9e87dcb0e https://doi.org/10.1007/s11633-025-1581-1

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Multi-level Attention Network for Accurate Crowd Counting in Challenging Rail Transit Environment

Key Points

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Implication

Discussion

Cite This Study

Multi-level Attention Network for Accurate Crowd Counting in Challenging Rail Transit Environment

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

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