Bayesian Modeling of Crowd Dynamics by Aggregating Multiresolution Observations From UAVs and UGVs

Yifei Yuan, Young Jun Son, Jian Liu

Research output: Contribution to journalArticlepeer-review

Abstract

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) can be jointly deployed to form a collaborative surveillance system, where UAVs collect low-resolution images at a high altitude to obtain a global perception and UGVs observe high-resolution images within a focused detection range. Such multiresolution heterogeneous observations create opportunities yet pose challenges to model the dynamics of the targeted crowds. Existing approaches that integrate multiresolution observations rely on intensive computation of a large volume of historical data, and thus, result in a lack of computational efficiency, accuracy, and robustness. To address these limitations, this article proposes a new crowd dynamics modeling approach to aggregating the multiresolution information under a Bayesian inference framework. Beta-binomial and Normal-Wishart conjugate distributions were adopted to model crowd dynamics from low-resolution UAV observations and high-resolution UGV observations, respectively. Based on the proposed approach, a real-time model updating mechanism is developed to implement information aggregation for onboard crowd dynamics inference, where high efficiency, accuracy, and robustness are critical. Numerical simulations and onboard experiments were conducted to demonstrate the effectiveness and efficiency of the proposed modeling approach.

Original languageEnglish (US)
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
DOIs
StateAccepted/In press - 2022
Externally publishedYes

Keywords

  • Computational modeling
  • Crowd surveillance
  • Data models
  • Dynamics
  • information aggregation
  • Load modeling
  • model updating
  • Predictive models
  • prior elicitation
  • Surveillance
  • Vehicle dynamics

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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