PADS: A probabilistic activity detection framework for video data

Massimiliano Albanese*, Rama Chellappa, Naresh Cuntoor, Vincenzo Moscato, Antonio Picariello, V. S. Subrahmanian, Octavian Udrea

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


There is now a growing need to identify various kinds of activities that occur in videos. In this paper, we first present a logical language called Probabilistic Activity Description Language (PADL) in which users can specify activities of interest. We then develop a probabilistic framework which assigns to any subvideo of a given video sequence a probability that the subvideo contains the given activity, and we finally develop two fast algorithms to detect activities within this framework. OffPad finds all minimal segments of a video that contain a given activity with a probability exceeding a given threshold. In contrast, the OnPad algorithm examines a video during playout (rather than afterwards as OffPad does) and computes the probability that a given activity is occurring (even if the activity is only partially complete). Our prototype Probabilistic Activity Detection System (PADS) implements the framework and the two algorithms, building on top of existing image processing algorithms. We have conducted detailed experiments and compared our approach to four different approaches presented in the literature. We show thatfor complex activity definitionsour approach outperforms all the other approaches.

Original languageEnglish (US)
Article number5401166
Pages (from-to)2246-2261
Number of pages16
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number12
StatePublished - 2010
Externally publishedYes


  • Applications and expert knowledge-intensive systems
  • applications
  • computer vision
  • image processing and computer vision
  • video analysis
  • vision and scene understanding

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics


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