Synchronized Data Collection for Human Group Recognition

It is commonplace for people to perform various kinds of activities in groups. The recognition of human groups is of importance in many applications including crowd evacuation, teamwork coordination, and advertising. Existing group recognition approaches require snapshots of human trajectories, whic...

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Autores principales:	Weiping Zhu, Lin Xu, Yijie Tang, Rong Xie
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	group recognition synchronization trajectory interpolation message passing Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ee2a0a9ff6ca4a98ba24dcf097595a62
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spelling	oai:doaj.org-article:ee2a0a9ff6ca4a98ba24dcf097595a622021-11-11T19:06:26ZSynchronized Data Collection for Human Group Recognition10.3390/s212170941424-8220https://doaj.org/article/ee2a0a9ff6ca4a98ba24dcf097595a622021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7094https://doaj.org/toc/1424-8220It is commonplace for people to perform various kinds of activities in groups. The recognition of human groups is of importance in many applications including crowd evacuation, teamwork coordination, and advertising. Existing group recognition approaches require snapshots of human trajectories, which is often impossible in the reality due to different data collection start time and frequency, and the inherent time deviations of devices. This study proposes an approach to synchronize the data of people for group recognition. All people’s trajectory data are aligned by using data interpolating. The optimal interpolating points are computed based on our proposed error function. Moreover, the time deviations among devices are estimated and eliminated by message passing. A real-life data set is used to validate the effectiveness of the proposed approach. The results show that 97.7% accuracy of group recognition can be achieved. The approach proposed to deal with time deviations was also proven to lead to better performance compared to that of the existing approaches.Weiping ZhuLin XuYijie TangRong XieMDPI AGarticlegroup recognitionsynchronizationtrajectory interpolationmessage passingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7094, p 7094 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	group recognition synchronization trajectory interpolation message passing Chemical technology TP1-1185
spellingShingle	group recognition synchronization trajectory interpolation message passing Chemical technology TP1-1185 Weiping Zhu Lin Xu Yijie Tang Rong Xie Synchronized Data Collection for Human Group Recognition
description	It is commonplace for people to perform various kinds of activities in groups. The recognition of human groups is of importance in many applications including crowd evacuation, teamwork coordination, and advertising. Existing group recognition approaches require snapshots of human trajectories, which is often impossible in the reality due to different data collection start time and frequency, and the inherent time deviations of devices. This study proposes an approach to synchronize the data of people for group recognition. All people’s trajectory data are aligned by using data interpolating. The optimal interpolating points are computed based on our proposed error function. Moreover, the time deviations among devices are estimated and eliminated by message passing. A real-life data set is used to validate the effectiveness of the proposed approach. The results show that 97.7% accuracy of group recognition can be achieved. The approach proposed to deal with time deviations was also proven to lead to better performance compared to that of the existing approaches.
format	article
author	Weiping Zhu Lin Xu Yijie Tang Rong Xie
author_facet	Weiping Zhu Lin Xu Yijie Tang Rong Xie
author_sort	Weiping Zhu
title	Synchronized Data Collection for Human Group Recognition
title_short	Synchronized Data Collection for Human Group Recognition
title_full	Synchronized Data Collection for Human Group Recognition
title_fullStr	Synchronized Data Collection for Human Group Recognition
title_full_unstemmed	Synchronized Data Collection for Human Group Recognition
title_sort	synchronized data collection for human group recognition
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ee2a0a9ff6ca4a98ba24dcf097595a62
work_keys_str_mv	AT weipingzhu synchronizeddatacollectionforhumangrouprecognition AT linxu synchronizeddatacollectionforhumangrouprecognition AT yijietang synchronizeddatacollectionforhumangrouprecognition AT rongxie synchronizeddatacollectionforhumangrouprecognition
_version_	1718431619955556352

Synchronized Data Collection for Human Group Recognition

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