Arbab-Zavar, Banafshe, Bouchrika, Imed, Carter, John and Nixon, Mark
On Supervised Human Activity Analysis for Structured Environments
At 6th International Symposium on Visual Computing (ISVC10), United States.
01 Nov - 01 Dec 2010.
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.
Conference or Workshop Item
||Event Dates: November - December, 2010
|Venue - Dates:
||6th International Symposium on Visual Computing (ISVC10), United States, 2010-11-01 - 2010-12-01
||Vision, Learning and Control
||07 Dec 2010 16:41
||17 Apr 2017 18:07
|Further Information:||Google Scholar|
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