Evidential event inference in transport video surveillance

Xin Hong, Yan Huang, WenJun Ma, Sriram Varadarajan, Paul Miller, Weiru Liu, Maria Jose Santofimia Romero, Jesus Martinez del Rincon, Huiyu Zhou

Research output: Contribution to journalArticle (Academic Journal)peer-review

10 Citations (Scopus)
311 Downloads (Pure)


This paper presents a new framework for multi-subject event inference in surveillance video, where measurements produced by low-level vision analytics usually are noisy, incomplete or incorrect. Our goal is to infer the composite events undertaken by each subject from noise observations. To achieve this, we consider the temporal characteristics of event relations and propose a method to correctly associate the detected events with individual subjects. The Dempster–Shafer (DS) theory of belief functions is used to infer events of interest from the results of our vision analytics and to measure conflicts occurring during the event association. Our system is evaluated against a number of videos that present passenger behaviours on a public transport platform namely buses at different levels of complexity. The experimental results demonstrate that by reasoning with spatio-temporal correlations, the proposed method achieves a satisfying performance when associating atomic events and recognising composite events involving multiple subjects in dynamic environments.
Original languageEnglish
Pages (from-to)276-297
Number of pages22
JournalComputer Vision and Image Understanding
Issue numberC
Early online date1 Apr 2016
Publication statusPublished - 2016


  • Transport surveillance
  • Video events
  • Event modelling
  • Reasoning under uncertainty
  • Spatio-temporal constraint
  • Minimum conflict optimisation
  • Event association
  • event recognition


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