Stephens, Kyle and Bors, Adrian Gheorghe orcid.org/0000-0001-7838-0021 (2017) Human Group Activity Recognition based on Modelling Moving Regions Interdependencies. In: Proc. of International Conference on Pattern Recognition (ICPR). IEEE , pp. 2116-2121.
Abstract
n this research study, we model the interdepen- dency of actions performed by people in a group in order to identify their activity. Unlike single human activity recognition, in interacting groups the local movement activity is usually influenced by the other persons in the group. We propose a model to describe the discriminative characteristics of group activity by considering the relations between motion flows and the locations of moving regions. The inputs of the proposed model are jointly represented in time-space and time-movement spaces. These spaces are modelled using Kernel Density Estimation (KDE) which is then fed into a machine learning classifier. Unlike in other group-based human activity recognition algorithms, the proposed methodology is automatic and does not rely on any pedestrian detection or on the manual annotation of tracks. Index Terms —Group Activity Identification, Motion Segme
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Copyright 2016 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 20 Feb 2018 15:10 |
Last Modified: | 16 Oct 2024 10:57 |
Published Version: | https://doi.org/10.1109/ICPR.2016.7899948 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICPR.2016.7899948 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127769 |
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Description: Human Group Activity Recognition based on Modelling Moving Regions Interdependencies