Recognition of Group Activities using Dynamic Probabilistic Networks

  • Authors:
  • Shaogang Gong;Tao Xiang

  • Affiliations:
  • -;-

  • Venue:
  • ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
  • Year:
  • 2003

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Abstract

Dynamic Probabilistic Networks (DPNs) are exploitedfor modelling the temporal relationships among a set of differentobject temporal events in the scene for a coherentand robust scene-level behaviour interpretation. In particular,we develop a Dynamically Multi-Linked Hidden MarkovModel (DML-HMM) to interpret group activities involvingmultiple objects captured in an outdoor scene. The model isbased on the discovery of salient dynamic interlinks amongmultiple temporal events using DPNs. Object temporalevents are detected and labelled using Gaussian MixtureModels with automatic model order selection. A DML-HMMis built using Schwarz's Bayesian Information Criterionbased factorisation resulting in its topology being intrinsicallydetermined by the underlying causality and temporalorder among different object events. Our experimentsdemonstrate that its performance on modelling group activitiesin a noisy outdoor scene is superior compared to thatof a Multi-Observation Hidden Markov Model (MOHMM),a Parallel Hidden Markov Model (PaHMM) and a CoupledHidden Markov Model (CHMM).