DocumentCode :
1361182
Title :
Video Event Modeling and Recognition in Generalized Stochastic Petri Nets
Author :
Lavee, Gal ; Rudzsky, Michael ; Rivlin, Ehud ; Borzin, Artyom
Author_Institution :
Dept. of Comput. Sci., Technion - Israel Inst. of Technol., Haifa, Israel
Volume :
20
Issue :
1
fYear :
2010
Firstpage :
102
Lastpage :
118
Abstract :
In this paper, we propose the surveillance event recognition framework using Petri Nets (SERF-PN) for recognition of event occurrences in video. The Petri Net (PN) formalism allows a robust way to express semantic knowledge about the event domain as well as efficient algorithms for recognizing events as they occur in a particular video sequence. The major novelties of this paper are extensions to both the modeling and the recognition capacities of the Object PN paradigm. The first contribution of this paper is the extension of the PN representational capacities by introducing stochastic timed transitions to allow modeling of events which have some variance in duration. These stochastic timed transitions sample the duration of the condition from a parametrized distribution. The parameters of this distribution can be specified manually or learned from available video data. A second representational novelty is the use of a single PN to represent the entire event domain, as opposed to previous approaches which have utilized several networks, one for each event of interest. A third contribution of this paper is the capacity to probabilistically predict future events by constructing a discrete time Markov chain model of transitions between states. The experiments section of the paper thoroughly evaluates the application of the SERF-PN framework in the event domains of surveillance and traffic monitoring and provides comparison to other approaches using the CAVIAR dataset , a standard dataset for video analysis applications.
Keywords :
Markov processes; Petri nets; discrete time systems; video signal processing; CAVIAR dataset; Petri net formalism; discrete time Markov chain model; event domain; event occurrences; generalized stochastic Petri nets; parametrized distribution; semantic knowledge; stochastic timed transition; surveillance event recognition framework; video analysis application; video event modeling; video sequence; Action; Petri Net; activity; behavior; event; scenario; video analysis;
fLanguage :
English
Journal_Title :
Circuits and Systems for Video Technology, IEEE Transactions on
Publisher :
ieee
ISSN :
1051-8215
Type :
jour
DOI :
10.1109/TCSVT.2009.2031372
Filename :
5229241
Link To Document :
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