Learning to recognize video-based spatiotemporal events

Harini Veeraraghavan, Nikolaos P. Papanikolopoulos

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


A key research issue in activity recognition in real-world applications, such as in intelligent transportation systems (ITS), is to automatically learn robust models of activities that require minimal human training. In this paper, we contribute a novel approach for learning sequenced spatiotemporal activities in outdoor traffic intersections. Concretely, by representing the activities as sequences of actions, we contribute a semisupervised learning algorithm that learns activities as complete stochastic context-free grammars (SCFGs), namely, the grammar structure and the parameters. Our approach has been implemented and tested on real-world scenes, and we present experimental results of the grammar learning and activity recognition applied to datacollection and traffic monitoring applications using video data.

Original languageEnglish (US)
Article number5166486
Pages (from-to)628-638
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number4
StatePublished - Dec 1 2009


  • Context-free grammars
  • Intelligent transportation system (ITS) applications
  • Machine learning
  • Vehicle tracking
  • Video analysis

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