An A priori-based Method for Frequent Composite Event Discovery in Videos

LES AUTEURS : A. Toshev, F. Brémond and M. Thonnat

RESUME:

We propose a method for discovery of composite events in videos. The algorithm processes a set of primitive events such as simple spatial relations between objects obtained from a tracking system and outputs frequent event patterns which can be interpreted as frequent composite events. We use the "Apriori" algorithm from the field of data mining for efficient detection of frequent patterns. We adapt this algorithm to handle temporal uncertainty
in the data without losing its computational effectiveness. It is formulated as a generic framework in which the context knowledge is clearly separated from the method in form of a similarity measure for comparison between two video activities and a library
of primitive events serving as a basis for the composite events

Mots clé: Behaviour Recognition, Unsurpervised learning technics.

Pour télécharger cet article,  cliquez ici

BibTeX reference:

@InProceedings{Toshev2006,
author = {A. Toshev,F. Br\'emond and M. Thonnat},
title = {An A priori-based Method for Frequent Composite Event Discovery in Videos},
booktitle = {to appear in Proceedings of the International Conference on Computer Vision Systems (ICVS'06)},
address = {New-York, NY, USA},
month = {January},
year = {2006},
}