Author(s)

Zhang Dongming ¹, Tian Xueqing ¹, Zhao Wenhui ¹, Guo Yihan ¹, Chen Lijia ¹

Affiliation(s)

¹ School of Physics and Electronics, Henan University, Kaifeng, 475004, China

Corresponding Author

Tian Xueqing

ABSTRACT

Crowd counting and precise localization in dense scenes are critical tasks in computer vision. Although the point-based prediction framework P2PNet eliminates complex post-processing steps through ensemble prediction, it still faces challenges such as insufficient multi-scale feature extraction and limited ability to perceive key information in complex scenes. To address these issues, this paper improves P2PNet by proposing a model based on a far-near network and a dynamic dual attention mechanism. Specifically, it introduces a far-near adaptive network (FN-Net) and a dynamic dual attention mechanism (DDAM). FN-Net explicitly models continuous scale variations caused by perspective effects by dividing the image into regions based on spatial position and assigning differentiated receptive fields. DDAM focuses on crowded areas through parallel spatial attention and channel attention sub-modules, selects discriminative features, and integrates a dynamic weighted fusion mechanism to adaptively combine the advantages of both attentions. Experiments show that our approach effectively enhances key features while suppressing background noise thus improves crowd counting accuracy.

KEYWORDS

Crowd counting; Point localization; Multi-scale feature modelling; Attention mechanism; Deep learning

CITE THIS PAPER

Zhang Dongming, Tian Xueqing, Zhao Wenhui, Guo Yihan, Chen Lijia, NF-Net: Crowd Counting Based on Near-Far Network and Dynamic Dual Attention Mechanism. Journal of Artificial Intelligence Practice (2025) Vol. 8: 25-31. DOI: http://dx.doi.org/10.23977/jaip.2025.080404.

REFERENCES

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[3] Li, Y., Zhang, X. and Chen, D. (2018) CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1, 1-9.
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