首页|Self-attention Guidance Based Crowd Localization and Counting

Self-attention Guidance Based Crowd Localization and Counting

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Most existing studies on crowd analysis are limited to the level of counting,which cannot provide the exact location of indi-viduals.This paper proposes a self-attention guidance based crowd localization and counting network(SA-CLCN),which can simultan-eously locate and count crowds.We take the form of object detection,using the original point annotations of crowd datasets as supervi-sion to train the network.Ultimately,the center point coordinate of each head as well as the number of crowds are predicted.Specific-ally,to cope with the spatial and positional variations of the crowd,the proposed method introduces transformer to construct a global-local feature extractor(GLFE)together with the convolutional structure.It establishes the near-to-far dependency between elements so that the global context and local detail features of the crowd image can be extracted simultaneously.Then,this paper designs a pyramid feature fusion module(PFFM)to fuse the global and local information from high level to low level to obtain a multiscale feature repres-entation.In downstream tasks,this paper predicts candidate point offsets and confidence scores by a simple regression header and classi-fication header.In addition,the Hungarian algorithm is used to match the predicted point set and the labelled point set to facilitate the calculation of losses.The proposed network avoids the errors or higher costs associated with using traditional density maps or bounding box annotations.Importantly,we have conducted extensive experiments on several crowd datasets,and the proposed method has pro-duced competitive results in both counting and localization.

Crowd localizationcrowd countingtransformerpoint supervisionobject detection

Zhouzhou Ma、Guanghua Gu、Wenrui Zhao

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School of Information Science and Engineering,Yanshan University,Qinhuangdao 066000,China

Hebei Key Laboratory of Information Transmission and Signal Processing,Qinhuangdao 066000,China

National Natural Science Foundation of ChinaNatural science Foundation of Hebei Province,ChinaHebei Key Laboratory Project,China

62072394F2021203019202250701010046

2024

10.1007/s11633-023-1428-6

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(5)