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2020 | OriginalPaper | Buchkapitel

6. Group Behavior Recognition

verfasst von : Zhiwen Yu, Zhu Wang

Erschienen in: Human Behavior Analysis: Sensing and Understanding

Verlag: Springer Singapore

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Abstract

Compared with individual behavior sensing and understanding, the recognition of group behavior is more challenging. In this chapter, we present some of our recent progress on group behavior sensing and recognition. Specifically, in Sect. 6.1, we discuss how to recognize the mobility level and structure of groups in the physical world by leveraging mobile devices. Afterwards, we present the recognition of human semantic interactions and group interaction patterns in smart spaces in Sects. 6.2 and 6.3. Finally, we discuss how we can organize, suggest, or predict group activities by leveraging the power of mobile crowdsensing and mobile social networking in Sects. 6.4 and 6.5.

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Vorheriges Kapitel Individual Behavior Recognition
Nächstes Kapitel Community Behavior Understanding
Fußnoten
1
Part of this section is based on a prevous work: H. Du, Z. Yu, F. Yi, Z. Wang, Q. Han and B. Guo, “Recognition of Group Mobility Level and Group Structure with Mobile Devices,” in IEEE Transactions on Mobile Computing, vol. 17, no. 4, pp. 884–897, 1 April 2018. DOI: https://​doi.​org/​10.​1109/​TMC.​2017.​2694839
 
2
Part of this section is based on a previous work: Z. Yu, X. Zhou and Y. Nakamura, “Extracting Social Semantics from Multimodal Meeting Content,” in IEEE Pervasive Computing, vol. 12, no. 2, pp. 68–75, April–June 2013. DOI: https://​doi.​org/​10.​1109/​MPRV.​2012.​55
 
3
Part of this section is based on a previous work: Z. Yu, Z. Yu, H. Aoyama, M. Ozeki and Y. Nakamura, “Capture, recognition, and visualization of human semantic interactions in meetings,” 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom), Mannheim, 2010, pp. 107–115. DOI: https://​doi.​org/​10.​1109/​PERCOM.​2010.​5466987
 
4
Part of this section is based on a previous work: B. Guo, Z. Yu, L. Chen, X. Zhou and X. Ma, “MobiGroup: Enabling Lifecycle Support to Social Activity Organization and Suggestion With Mobile Crowd Sensing,” in IEEE Transactions on Human-Machine Systems, vol. 46, no. 3, pp. 390–402, June 2016. DOI: https://​doi.​org/​10.​1109/​THMS.​2015.​2503290
 
5
Part of this section is based on a previous work: R. Du, Z. Yu, T. Mei, Z.T. Wang, Z. Wang, B, Guo. Predicting activity attendance in event-based social networks: content, context and social influence, Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, USA, September, 2014, pp. 425–434
 
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Metadaten
Titel
Group Behavior Recognition
verfasst von
Zhiwen Yu
Zhu Wang
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Human Behavior Analysis: Sensing and Understanding

Print ISBN: 978-981-15-2108-9

Electronic ISBN: 978-981-15-2109-6

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-2109-6_6