Surveillance Event Interpretation Using Generalized Stochastic Petri Nets

Authors:
Artyom Borzin;Ehud Rivlin;Michael Rudzsky
Affiliations:
Technion-Israel Institute of Technology 32000, Haifa, Israel;Technion-Israel Institute of Technology 32000, Haifa, Israel;Technion-Israel Institute of Technology 32000, Haifa, Israel
Venue:
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
Year:
2007

Citing 0
Cited 3

Understanding video events: a survey of methods for automatic interpretation of semantic occurrences in video

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Building Petri nets from video event ontologies

ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Determining the best suited semantic events for cognitive surveillance

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 results of an external module for object detection, tracking and classification. The proposed approach is evaluated using the developed surveillance system which can recognize events from videos and give a textual expression for the detected behavior. The experimental results illustrate the ability of the system to create complex spatiotemporal relations and to recognize the behavior of one or multiple objects in various video scenes.