A constrained probabilistic Petri Net framework for human activity detection in video

Massimiliano Albanese, Rama Chellappa, Vincenzo Moscato, Antonio Picariello, V. S. Subrahmanian, Pavan Turaga, Octavian Udrea

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


Recognition of human activities in restricted settings such as airports, parking lots and banks is of significant interest in security and automated surveillance systems. In such settings, data is usually in the form of surveillance videos with wide variation in quality and granularity. Interpretation and identification of human activities requires an activity model that a) is rich enough to handle complex multi-agent interactions, b) is robust to uncertainty in low-level processing and c) can handle ambiguities in the unfolding of activities. We present a computational framework for human activity representation based on Petri nets. We propose an extensionProbabilistic Petri Nets (PPN)and show how this model is well suited to address each of the above requirements in a wide variety of settings. We then focus on answering two types of questions: (i) what are the minimal sub-videos in which a given activity is identified with a probability above a certain threshold and (ii) for a given video, which activity from a given set occurred with the highest probability? We provide the PPN-MPS algorithm for the first problem, as well as two different algorithms (naive PPN-MPA and PPN-MPA) to solve the second. Our experimental results on a dataset consisting of bank surveillance videos and an unconstrained TSA tarmac surveillance dataset show that our algorithms are both fast and provide high quality results.

Original languageEnglish (US)
Article number4694845
Pages (from-to)1429-1443
Number of pages15
JournalIEEE Transactions on Multimedia
Issue number8
StatePublished - Dec 2008
Externally publishedYes


  • Algorithms
  • Machine vision
  • Petri nets
  • Surveillance

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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