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.
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.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS) |
| Publisher | IEEE |
| Pages | 59-65 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - Aug 2016 |