By the same authors

Human Group Activity Recognition based on Modelling Moving Regions Interdependencies

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Publication details

Title of host publicationProc. of International Conference on Pattern Recognition (ICPR)
DateE-pub ahead of print - 8 Dec 2016
DatePublished (current) - 27 Apr 2017
Pages2116-2121
Number of pages6
PublisherIEEE
Original languageEnglish

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

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