Cross-Scene Point Prediction Crowd Counting Method Based on Meta-Weight-Net

XU Xin; TAN Zhuo-lin; GAO Chen-qiang; XI Yue

doi:10.12263/DZXB.20250285

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Cross-Scene Point Prediction Crowd Counting Method Based on Meta-Weight-Net

PAPERS | 更新时间：2025-12-27

- Cross-Scene Point Prediction Crowd Counting Method Based on Meta-Weight-Net
- ACTA ELECTRONICA SINICA Vol. 53, Issue 9, Pages: 3371-3383(2025)
- 作者机构：
  
  1.重庆邮电大学通信与信息工程学院，重庆 400065
  2.澳门大学科技学院，澳门 999078
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2022YFA1004100)
- DOI：10.12263/DZXB.20250285
  CLC： TN911.73;TP391
- Received：14 April 2025，
  
  Accepted：22 August 2025，
  
  Published：25 September 2025
- 稿件说明：
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徐昕, 谭卓林, 高陈强, 等. 基于元权重网络的跨场景点预测人群计数方法[J]. 电子学报, 2025, 53(09): 3371-3383.

XU Xin, TAN Zhuo-lin, GAO Chen-qiang, et al. Cross-Scene Point Prediction Crowd Counting Method Based on Meta-Weight-Net[J]. Acta Electronica Sinica, 2025, 53(09): 3371-3383.
徐昕, 谭卓林, 高陈强, 等. 基于元权重网络的跨场景点预测人群计数方法[J]. 电子学报, 2025, 53(09): 3371-3383. DOI：10.12263/DZXB.20250285

XU Xin, TAN Zhuo-lin, GAO Chen-qiang, et al. Cross-Scene Point Prediction Crowd Counting Method Based on Meta-Weight-Net[J]. Acta Electronica Sinica, 2025, 53(09): 3371-3383. DOI：10.12263/DZXB.20250285

摘要

跨场景人群计数由于光照、尺度、拍摄角度和人群密度等因素引起的数据分布差异，导致在不同场景下的计数精度下降.针对现有人群计数模型在跨场景应用时存在的问题，本文提出了一种基于元学习的场景感知重新加权方法.该方法通过设计点预测计数模型直接预测每个行人的精确坐标，避免了传统密度图方法的定位模糊问题.元权重网络从元数据中学习显式点预测损失的加权方案，通过场景感知分支将每个场景视为一个单独的学习任务，利用不同场景的内在特征实现自适应的加权方案，降低标注噪声对模型跨场景泛化能力的影响.此外，针对现有数据集在教学领域的局限性构建了新的校园多场景人群计数数据集（Multi-Scene Crowd counting dataset，MS-Crowd），为跨场景研究提供了更全面的评估基准.实验结果表明该方法在MS-Crowd和户外公开数据集ShanghaiTech上的平均绝对误差（Mean Absolute Error，MAE）分别降低了19.7%和10.7%，验证了方法的有效性.

Abstract

Cross-scene crowd counting often suffers from degraded accuracy due to data distribution disparities caused by factors such as illumination

scale

camera angles

and crowd density. To address the limitations of existing crowd counting models in cross-scene applications

a meta-learning-based scene-aware reweighting method is proposed. Instead of relying on traditional density map approaches that suffer from localization ambiguity

the method employs a point prediction counting model to directly estimate the precise coordinates of each individual. A meta-weight network is introduced to learn an explicit weighting scheme for the point prediction loss from meta-data

while a scene-aware branch treats each scene as an independent learning task

leveraging intrinsic features across scenes to adaptively adjust the weighting scheme and mitigate the impact of annotation noise on cross-scene generalization. Furthermore

to overcome the limitations of existing datasets in educational settings

a new campus multi-scene crowd counting dataset (MS-Crowd) is constructed

providing a more comprehensive benchmark for cross-scene evaluation. Experimental results demonstrate that the proposed method reduces the mean absolute error (MAE) by 19.7% and 10.7% on the MS-Crowd and the public outdoor dataset ShanghaiTech

respectively

validating its effectiveness.

关键词

Keywords

references

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