Artyom Borzin

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In this paper we propose the Surveillance Event Recognition Framework using Petri Nets (SERF-PN) for recognition of event occurrences in video based on the Petri Net(PN) formalism. This formalism allows us 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(More)
In this paper we present video event representation and recognition approaches that are based on Generalized Stochastic Petri Nets (GSPN). Along with the typical modeling capabilities of GSPN for video recognition, we propose to integrate the Petri net marking analysis for better scene understanding. This work focuses on behavior modeling and uses the(More)
VEML) to annotate instances of the events described in VERL [2]. Another representation technique base on hierarThis paper presents a novel approach for video event reprechical CASE representation was proposed by M Shah et al. sentation and recognition of multi agent interactions. The in [3] and then enhanced by [4]. proposed approach integrates behavior(More)
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