BaG: Behavior-Aware Group Detection in Crowded Urban Spaces Using WiFi Probes

Jiaxing SHEN*, Jiannong CAO, Xuefeng LIU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

7 Citations (Scopus)


Group detection is gaining popularity as it enables variousXzX applications ranging from marketing to urban planning. Existing methods use received signal strength indicator (RSSI) to detect co-located people as groups. However, this approach might have difficulties in crowded urban spaces since many strangers with similar mobility patterns could be identified as groups. Moreover, RSSI is vulnerable to many factors like the human body attenuation and thus is unreliable in crowded scenarios. In this work, we propose a behavior-aware group detection system (BaG). BaG fuses people's mobility information and smartphone usage behaviors. We observe that people in a group tend to have similar phone usage patterns. Those patterns could be effectively captured by the proposed feature: number of bursts (NoB). Unlike RSSI, NoB is more resilient to environmental changes as it only cares about receiving packets or not. Besides, both mobility and usage patterns correspond to the same underlying grouping information. We propose a detection method based on collective matrix factorization to reveal the hidden associations by factorizing mobility information and usage patterns simultaneously. Experimental results indicate BaG outperforms baseline approaches by $3.97\% \sim 15.79\%$3.97%∼15.79% in F-score. The proposed system could also achieve robust and reliable performance in scenarios with different levels of crowdedness.

Original languageEnglish
Pages (from-to)3298-3310
Number of pages13
JournalIEEE Transactions on Mobile Computing
Issue number12
Early online date2 Jun 2020
Publication statusPublished - Dec 2021
Externally publishedYes

Bibliographical note

This work was supported by the Key-Area Research and Development Program of Guangdong Province (2020B010164002). It was also supported by RGC General Research Fund (GRF) 2018/19 (PolyU 152133/18), HK RGC Collaborative Research Fund (CRF) 2018/19 - Group Research Grant (RGC No.: C6030-18G), PolyU Internal Start-up Fund (P0035274), and the National Natural Science Foundation of China Grant (61976012).


  • collective matrix factorization
  • Group detection
  • probe request
  • WiFi


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