Abstract
In this research study we adopt a probabilistic modelling of interactions in groups of people, using video sequences, leading to the recognition of their activities. Firstly, we model short smooth streams of localised movement. Afterwards, we partition the scene in regions of distinct movement, by using maximum a posteriori estimation, by fitting Gaussian Mixture Models (GMM) to the movement statistics. Interactions between moving regions are modelled using the Kullback–Leibler (KL) divergence between pairs of statistical representations of moving regions. Such interactions are considered with respect to the relative movement, moving region location and relative size, as well as to the dynamics of the movement and location inter-dependencies, respectively. The proposed methodology is assessed on two different data sets showing different categories of human interactions and group activities.
| Original language | English |
|---|---|
| Pages (from-to) | 34–46 |
| Number of pages | 13 |
| Journal | Digital Signal Processing |
| Volume | 79 |
| Early online date | 17 Apr 2018 |
| DOIs | |
| Publication status | Published - Aug 2018 |
Bibliographical note
© 2018 Elsevier Inc. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for detailsKeywords
- Human Interactions
- Human Group Activity
- Kullback–Leiblerdivergence
- Kernel Density Estimatio
- Gaussian Mixture Models