Stephens, Kyle and Bors, Adrian Gheorghe orcid.org/0000-0001-7838-0021 (2016) Group Activity Recognition on Outdoor Scenes. In: IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). IEEE , pp. 59-65.
Abstract
In this research study, we propose an automatic group activity recognition approach by modelling the interdependencies of group activity features over time. Unlike in simple human activity recognition approaches, the distinguishing characteristics of group activities are often determined by how the movement of people are influenced by one another. We propose to model the group interdependences in both motion and location spaces. These spaces are extended to time-space and time-movement spaces and modelled us- ing Kernel Density Estimation (KDE). Such representations are then fed into a machine learning classifier which iden- tifies the group activity. Unlike other approaches to group activity recognition, we do not rely on the manual annota- tion of pedestrian tracks from the video sequence.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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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: | 21 Feb 2018 09:50 |
Last Modified: | 24 Feb 2025 00:09 |
Published Version: | https://doi.org/10.1109/AVSS.2016.7738071 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/AVSS.2016.7738071 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127774 |
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Description: Group Activity Recognition on Outdoor Scenes