A framework of joint object tracking and event detection

Ruoyu Roy Wang, Thomas S Huang

Research output: Contribution to journalArticlepeer-review


This paper describes a probabilistic framework for simultaneously performing object tracking and event detection in monocular videos. Mathematically, we cast the problem of jointly tracking and detecting semantic events as a principled model-based search problem in a multi-dimensional state space, where the tracking trajectory and event type are discovered via maximum a posteriori (MAP) optimization. The benefit of this approach comes from its combined utilization of particle probabilistic representation, multiple hypothesis retention, efficient particle propagation, and temporal optimization. We present qualitative and quantitative results from realistic video sequences to demonstrate the effectiveness of this approach.

Original languageEnglish (US)
Pages (from-to)343-355
Number of pages13
JournalPattern Analysis and Applications
Issue number4
StatePublished - Aug 2005

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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