南通职业大学学报2025，Vol.39Issue(1)：72-79,8.DOI:10.3969/j.issn.1008-5327.2025.01.014
基于多维度感知特征金字塔的人群计数算法
A Multi-Dimensional Perception Feature Pyramid-Based Algorithm for Crowd Counting
陈慧1
作者信息
- 1. 无锡职业技术学院 集成电路学院,江苏 无锡 214121
- 折叠
摘要
Abstract
To address the challenges of scale variation and head misidentification in crowd counting,a multi-dimensional perception feature pyramid-based crowd counting algorithm is proposed.Building upon a feature pyramid encoder-decoder network,the algorithm uses feature aggregation modules to construct an efficient dual-decoding structure.By repeatedly fusing semantic information from adjacent hierarchical levels,the net-work retains fine-grained features across different scales to better adapt to head size variations.Furthermore,a multi-dimensional perception module is incorporated at the apex of the network,which aggregates key head features from multiple dimensions,such as spatial and channel,and updates the feature weights at different positions.This not only effectively distinguishes head information from background but also further narrows prediction ranges for individual targets with multi-level supervision for holistic network training.The results of qualitative and quantitative analyses show that the proposed algorithm achieves superior performance across four public benchmark datasets.关键词
人群计数/多维度感知特征金字塔/卷积神经网络/注意力机制/特征融合Key words
crowd counting/multi-dimensional perception feature pyramid/convolutional neural network/attention mechanism/feature fusion分类
信息技术与安全科学引用本文复制引用
陈慧..基于多维度感知特征金字塔的人群计数算法[J].南通职业大学学报,2025,39(1):72-79,8.
