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Outline

Title
Abstract
Acknowledgements
Related Work
System Overview
Etiseo Dataset
Results
Conclusion
Introduction
Background
Database
Suitcase Classification
Alignment Procedure
References

Petri Net models for event recognition in surveillance videos

Profile image of Nagia GhanemNagia Ghanem

2007

Abstract

Video surveillance is the process of monitoring the behavior of people and objects within public places, e.g. airports and traffic intersections, by means of visual aids (cameras) usually for safety and security purposes. As the amount of video data gathered daily by surveillance cameras increases, the need for automatic systems to detect and recognize suspicious activities performed by people and objects is also increasing. The first part of the thesis describes a framework for modeling and recognition of events from surveillance video. Our framework is based on deterministic inference using Petri nets. Events can be composed by combining primitive events and previously defined events by spatial, temporal and logical relations. We provide a graphical user interface (GUI) to formulate such event models. Our approach automatically maps each of these models into a set of Petri net filters that represent the components of the event. Last but definitely not least, I would like to thank my two lovely daughters; Farah and Sarah for their unconditional love and patience during completing this thesis. iv TABLE OF CONTENTS List of Figures ix above and to the left of (x; y), inclusive. (b) The sum of the pixels within rectangle D is computed as: ii(4) + ii(1) − ii(2) − ii(3). .. .. .. . 4.4 Examples from Backpack Dataset Showing Different Ways of Holding the Backpack .

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<title>Mining tools for surveillance video</title>
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Representation and Recognition of Events in Surveillance Video Using Petri Nets
Nagia Ghanem

2004 Conference on Computer Vision and Pattern Recognition Workshop, 2004

Detection of events is an essential task in surveillance applications. This task requires finding a general event representation method and developing efficient recognition algorithms dealing with this representation. In this paper, we describe an interactive system for querying surveillance video about events. The queries may not be known in advance and have to be composed from primitive events and previously defined queries. We propose using Petri nets as both representation and recognition methods. The Petri net representation for users' queries is derived automatically from simpler event nets. Recognition is then performed by tokens moving through the Petri nets.

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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 video sequence. The major novelties of this paper are extensions to both the modeling and the recognition capacities of Object PN paradigm. The first contribution of this work 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 parametric distribution. The parameters of this distribution can be specified manually or learned from available video data. A second representational novelty of the paper 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 work 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

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