2010 | OriginalPaper | Buchkapitel
On Supervised Human Activity Analysis for Structured Environments
verfasst von : Banafshe Arbab-Zavar, Imed Bouchrika, John N. Carter, Mark S. Nixon
Erschienen in: Advances in Visual Computing
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
We consider the problem of developing an automated visual solution for detecting human activities within industrial environments. This has been performed using an overhead view. This view was chosen over more conventional oblique views as it does not suffer from occlusion, but still retains powerful cues about the activity of individuals. A simple blob tracker has been used to track the most significant moving parts i.e. human beings. The output of the tracking stage was manually labelled into 4 distinct categories: walking; carrying; handling and standing still which are taken together from the basic building blocks of a higher work flow description. These were used to train a decision tree using one subset of the data. A separate training set is used to learn the patterns in the activity sequences by Hidden Markov Models (HMM). On independent testing, the HMM models are applied to analyse and modify the sequence of activities predicted by the decision tree.