# PaDNet: Pan-Density Crowd Counting

arXiv: https://arxiv.org/abs/1811.02805 · HTML: https://mlsanity.com/paper/1811.02805/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 16 of 16)

- [Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework](https://mlsanity.com/paper/2107.12746/) (2021)
- [Discrete-Constrained Regression for Local Counting Models](https://mlsanity.com/paper/2207.09865/) (2022)
- [Redesigning Multi-Scale Neural Network for Crowd Counting](https://mlsanity.com/paper/2208.02894/) (2022)
- [Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark](https://mlsanity.com/paper/2107.08645/) (2021)
- [Spatiotemporal Dilated Convolution with Uncertain Matching for Video-based Crowd Estimation](https://mlsanity.com/paper/2101.12439/) (2021)
- [Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting](https://mlsanity.com/paper/2205.03307/) (2022)
- [Deep Imbalanced Regression via Hierarchical Classification Adjustment](https://mlsanity.com/paper/2310.17154/) (2023)
- [CrowdFormer: Weakly-supervised Crowd counting with Improved Generalizability](https://mlsanity.com/paper/2203.03768/) (2022)
- [Learning Discriminative Features for Crowd Counting](https://mlsanity.com/paper/2311.04509/) (2023)
- [Cross-head Supervision for Crowd Counting with Noisy Annotations](https://mlsanity.com/paper/2303.09245/) (2023)
- [Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting](https://mlsanity.com/paper/2203.06388/) (2022)
- [Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting](https://mlsanity.com/paper/2201.04819/) (2022)
- [Hybrid attention network based on progressive embedding scale-context for crowd counting](https://mlsanity.com/paper/2106.02324/) (2021)
- [Counting with Adaptive Auxiliary Learning](https://mlsanity.com/paper/2203.04061/) (2022)
- [Automatic counting of planting microsites via local visual detection and global count estimation](https://mlsanity.com/paper/2311.00796/) (2023)
- [L2HCount:Generalizing Crowd Counting from Low to High Crowd Density via Density Simulation](https://mlsanity.com/paper/2503.12935/) (2025)

## Abstract (arXiv)

The problem of counting crowds in varying density scenes or in different density regions of the same scene, named as pan-density crowd counting, is highly challenging. Previous methods are designed for single density scenes or do not fully utilize pan-density information. We propose a novel framework, the Pan-Density Network (PaDNet), for pan-density crowd counting. In order to effectively capture pan-density information, PaDNet has a novel module, the Density-Aware Network (DAN), that contains multiple sub-networks pretrained on scenarios with different densities. Further, a module named the Feature Enhancement Layer (FEL) is proposed to aggregate the feature maps learned by DAN. It learns an enhancement rate or a weight for each feature map to boost these feature maps. Further, we propose two refined metrics, Patch MAE (PMAE) and Patch RMSE (PRMSE), for better evaluating the model performance on pan-density scenarios. Extensive experiments on four crowd counting benchmark datasets indicate that PaDNet achieves state-of-the-art recognition performance and high robustness in pan-density crowd counting.