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On Supervised Human Activity Analysis for Structured Environments

On Supervised Human Activity Analysis for Structured Environments
On Supervised Human Activity Analysis for Structured Environments
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.
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Bouchrika, Imed
240fa05b-aed2-400a-a683-b4c0d20f2f68
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Bouchrika, Imed
240fa05b-aed2-400a-a683-b4c0d20f2f68
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Arbab-Zavar, Banafshe, Bouchrika, Imed, Carter, John and Nixon, Mark (2010) On Supervised Human Activity Analysis for Structured Environments. 6th International Symposium on Visual Computing (ISVC10), United States. 01 Nov - 01 Dec 2010.

Record type: Conference or Workshop Item (Poster)

Abstract

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.

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More information

Published date: November 2010
Additional Information: Event Dates: November - December, 2010
Venue - Dates: 6th International Symposium on Visual Computing (ISVC10), United States, 2010-11-01 - 2010-12-01
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 271757
URI: https://eprints.soton.ac.uk/id/eprint/271757
PURE UUID: 2a8133a0-825a-446e-9e77-8b7f37c90d20
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 07 Dec 2010 16:41
Last modified: 19 Jul 2019 01:24

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